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          KerasWrapper - AI for Pharo      Cache   Translate Page   Web Page Cache   
KerasWrapper is a project providing bindings from Pharo to Keras (which is implemented in Python). You can transparently play with the high-level neural networks API and visualize results in Roassal directly.

The github repo is on https://github.com/ObjectProfile/KerasWrapper
          Postdoctoral Position: Molecular Mechanisms and Neural Circuits of Memory and Behavior      Cache   Translate Page   Web Page Cache   

A postdoctoral position is available in the laboratory of Dr. Gleb Shumyatsky at Rutgers University.

A key question in research on memory and behavior is how the brain responds to environmental stimuli and a major challenge is the heterogeneity of brain cells and neural networks. We study activity-dependent molecular processes in the amygdala- and hippocampus-associated networks at the level of neural circuits and single cells using mouse genetics and transgenic tools. Our focus is on fear me…


          Nvidia AI can remove noise and artifacts from grainy photos      Cache   Translate Page   Web Page Cache   

Nvidia has developed an impressive deep learning technique capable of automatically removing noise and artifacts from photos. Whereas recent deep learning work in this field has focused on training a neural network with clean and noisy images, Nvidia’s AI can do so without ever being shown a noise-free example.

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          Test tube artificial neural network recognizes 'molecular handwriting'      Cache   Translate Page   Web Page Cache   

a droplet containing an artificial neural network illustration

Caltech scientists have developed an artificial neural network out of DNA that can recognize highly complex and noisy molecular information. 


Full story at http://www.caltech.edu/news/test-tube-artificial-neural-network-recognizes-molecular-handwriting-82679

Source
California Institute of Technology


This is an NSF News From the Field item.

          Scientists Invented AI Made From DNA – Motherboard      Cache   Translate Page   Web Page Cache   
Researchers made a neural network out of DNA that can recognize handwritten numbers. Source: Scientists Invented AI Made From DNA – Motherboard Comment – If I’m right about the future, this is where you want to put your money. Or, on the other hand, maybe you don’t! Advertisements
          An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution      Cache   Translate Page   Web Page Cache   

As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.

The post An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution appeared first on Uber Engineering Blog.


          Scientifique de données en mégadonnées - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Maîtrise des techniques analytiques appliquées (clustering, decision trees, neural networks, SVM (support vector machines), collaborative filtering, k-nearest...
From Intact - Sat, 30 Jun 2018 18:58:20 GMT - View all Montréal, QC jobs
          Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy      Cache   Translate Page   Web Page Cache   
Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with $w$ -event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying $w$ -event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two $w$ -event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.
          NVIDIA AI scrubs noise and watermarks from digital images      Cache   Translate Page   Web Page Cache   
NVIDIA researchers are back with yet another digital image technology that pushes the limits of traditional image manipulation. Unlike Adobe’s recently disclosed project, which involved a neural network trained to spot digitally altered images, NVIDIA’s newest creation can scrub digital sensor noise and watermarks from digital images. Thanks to the artificial intelligence powering it, the feature is far more effective … Continue reading
          NVIDIA AI scrubs noise and watermarks from digital images      Cache   Translate Page   Web Page Cache   
NVIDIA researchers are back with yet another digital image technology that pushes the limits of traditional image manipulation. Unlike Adobe’s recently disclosed project, which involved a neural network trained to spot digitally altered images, NVIDIA’s newest creation can scrub digital sensor noise and watermarks from digital images. Thanks to the artificial intelligence powering it, the feature is far more effective … Continue reading
          Using Siamese Networks and Pre-Trained Convolutional Neural Networks (CNNs) for Fashion Similarity Matching      Cache   Translate Page   Web Page Cache   
This post is co-authored by Erika Menezes, Software Engineer at Microsoft, and Chaitanya Kanitkar, Software Engineer at Twitter. This project was completed as part of the coursework for Stanford’s CS231n in Spring 2018. Ever seen someone wearing an interesting outfit and wonder where you could buy it yourself? You’re not alone – retailers world over... Read more
          NVIDIA AI scrubs noise and watermarks from digital images      Cache   Translate Page   Web Page Cache   
NVIDIA researchers are back with yet another digital image technology that pushes the limits of traditional image manipulation. Unlike Adobe’s recently disclosed project, which involved a neural network trained to spot digitally altered images, NVIDIA’s newest creation can scrub digital sensor noise and watermarks from digital images. Thanks to the artificial intelligence powering it, the feature is far more effective … Continue reading
          (USA-OR-Beaverton) Expert Information Security Data Scientist      Cache   Translate Page   Web Page Cache   
Become a Part of the NIKE, Inc. Team NIKE, Inc. does more than outfit the world's best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At Nike, it’s about each person bringing skills and passion to a challenging and constantly evolving game. Nike Technology designs, creates and implements the methods and tools needed to make the world’s largest sports brand run faster, smarter and more securely. Global Technology teams aggressively innovate the solutions needed to help employees navigate Nike's rapidly evolving landscape. From infrastructure to security and supply chain operations, Technology specialists drive growth through top-flight hardware, software and enterprise applications. Simply put, without Nike Technology, there are no Nike products. **Description** As an Artificial Intelligence Reinforcement Learning Data Scientist, your role in Corporate Information Security (CIS) Cyber Threat Analytics (CTA) team is to improve Nike’s Cyber Defense by leveraging your knowledge of data science, semantic and cognitive reasoning, neural networks, data mining, and industry best practices to develop detection methods. Your responsibilities will include: + Collaborate with data scientists and stakeholders to explore opportunities to develop data-driven solutions by developing and utilizing statistical modeling and machine learning algorithms + Translate ambiguous statements into structured problem statements and testable hypotheses + Analyze and profile available, reliable, and relevant data (internal and external) to uncover insights in support of scalable solutions + Clean, prepare and verify the integrity of data for analysis + Validate appropriate models to discover meaningful patterns and insights + Track model accuracy, ensuring model relevance and reliability + Create clear and structured deliverables to communicate meaningful insights and prescribe actionable data driven business solution recommendations + Build trust and drive business adoption by demystifying solution intricacies for stakeholders + Create clear, user-friendly documentation to support business usage of the solution + Collaborate with a team of data scientists, solutions delivery managers, engineers, and business stakeholders to take an opportunity or pain point from concept to scaled solution + Stay abreast of and apply industry and technology trends as well as emerging tools and techniques relevant to discipline + Participate in a continuous learning environment within the advanced analytics community through persistent development of new skills and sharing of knowledge through mentorships and contributions to the open source community **Qualifications** To make it clear, we're not looking for just anyone. We're looking for someone special, someone who had these experiences and clearly demonstrated these skills: + In-depth background in mathematics, statistics, or a related field + In-depth knowledge of applied data science + In-depth knowledge of machine learning algorithms and data science methods + In-depth data wrangling experience with structured and unstructured data + Experience with various programming and scripting languages + Experience with databases, processing, and storage frameworks + Experience with various hyperparameter tuning approaches + Strong knowledge of CRISP-DM and Agile data science framework + Strong knowledge of information security principles and practice. **Qualifications** To make it clear, we're not looking for just anyone. We're looking for someone special, someone who had these experiences and clearly demonstrated these skills: + In-depth background in mathematics, statistics, or a related field + In-depth knowledge of applied data science + In-depth knowledge of machine learning algorithms and data science methods + In-depth data wrangling experience with structured and unstructured data + Experience with various programming and scripting languages + Experience with databases, processing, and storage frameworks + Experience with various hyperparameter tuning approaches + Strong knowledge of CRISP-DM and Agile data science framework + Strong knowledge of information security principles and practice. NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world. NIKE, Inc. is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability. **Job ID:** 00398081 **Location:** United States-Oregon-Beaverton **Job Category:** Technology
          Scientifique de données en mégadonnées - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Maîtrise des techniques analytiques appliquées (clustering, decision trees, neural networks, SVM (support vector machines), collaborative filtering, k-nearest...
From Intact - Sat, 30 Jun 2018 18:58:20 GMT - View all Montréal, QC jobs
          BrainChip Names James Roe Director of North American Sales      Cache   Translate Page   Web Page Cache   
...Learning applications.  The Company has developed a revolutionary new spiking neural network technology that can learn autonomously, evolve and associate information just like the human brain. The technology, which is proprietary, is fast, completely digital and consumes very low power. The ...


          (USA-TX-Dallas) Robotics Computer Vision Engineer      Cache   Translate Page   Web Page Cache   
Robotics Computer Vision Engineer + Dallas, TX, USA + Full Time + Mobile Robotics Email Me This Job Join Bastian Solutions' R&D Mobile Robotics team in Dallas and be a part of the development of a Mobile Robotics solution for the Material Handling industry from the ground up. We are looking for a Robotics Computer Vision Engineer to join our R&D Mobile Robotics team in Dallas/Fort Worth. The goal will be to develop algorithms for advanced robotics system involving "pick and place" applications and object detection and recognition.New challenges will be presented daily which requires a diverse knowledge in many different disciplines - requiring the team to be fast moving and continuously growing. Responsibilities: + Develop novel, accurate computer vision algorithms that support advanced robotics applications. + Research, develop and prototype advanced hardware and software technologies related to tracking, 3D reconstruction, photometric stereo, object detection and appearance modeling + Apply machine learning to computer vision problems + Maintaining high level of communications with cross-functional team, vendors, and clients. Minimum qualifications: + BS in Computer Science, Computer Engineering, or related technical field + Experience developing computer vision software in C++, including algorithm design and systems software development + Experience with machine learning, Bayesian filtering, information theory and/or 3D geometry + Understanding of applied mathematics, numerical optimization, Signal Processing, and Object/Pattern Recognition + Prototyping skills Preferred qualifications: + MS degree in Computer Science, Computer Vision, Machine Learning, Robotics or related technical field + 3+ years' experience with real-time object tracking, SLAM, sensor fusion, real-time image processing, etc. + Experience training neural networks + Exposure to ROS (Robotic Operating System) – strongly desired. + Have a passion for robotics, a creative mind, and a desire to learn new technologies. Bastian Solutions, a Toyota Advanced Logistics company, is an independent material handling and robotics system integrator providing automated solutions for distribution, manufacturing, and order fulfillment centers around the world. We have over 60 years of experience building long term business partnerships. In addition to salary, commissions and incentive plans, we offer a wide variety of benefits, services and perks available to assist employees and their families in a variety of ways. These benefits help each individual maintain a good balance in their life. Other benefits of the role include: + Health, Dental, and Vision Insurance + 401(k) Retirement Plan with a company match + Vacation/Holiday Pay + Short/Long Term Disability Insurance + Tuition Reimbursement + Flexible Work Schedules + Volunteer Work + Professional Associations, Conferences and Subscriptions + Company Meetings & Events Join our growing team and sell, design and implement industry leading automated material handling solutions! #LI-SC1 Bastian Solutionshttps://bastiansolutions.hirecentric.com +
          멀티미디어공학 사이버 터치 글로브      Cache   Translate Page   Web Page Cache   
가상현실의 세계 (BBC 방송)[교육사회] 사회적발전, 문화적발전과 교육의 관계[교육정보화]교육정보화의 개념,변천, 교육정보화의 변화,기반구축, 교육정보화의 교사역할, 외국 사례, 교육정보화의 제고 방안[초등교육행정] 2003년 교육 시설 및 설비[교육공학] 국립 서울 과학관[교육공학] 현장적용을 위한 특수교육공학에 대한 연구 요 약 Abstract I. 서론 II. Cyber Touch Gloves의 사용 목적 목표 및 Solution II. Cyber Touch Gloves의 사용 목적 목표 및 Solution 3.1 개념 3.2 역사 3.3 cyber touch gloves = wired glove (유선장갑) 3.4 wired glove 역사 3.5 사용하는 방법을 볼수 있는 예 3.6 데이터 글로브의 구조 IV. 사용되는 기술 VI. 해당사례의 미래 전망 및 발전방향 참고문헌 3.6 데이터 글로브의 구조 그림1 데이터 글로브의 구조도 데이터 글로브 인터페이스 구조는 그림 1 에서와 같이 크게 촉각신호처리기 USB 인터페 이스 그리고 데이터 글러브로 구분된다. 데이터 글러브는 손동작에 따른 신호를 감지하는 센서 모듈과 센서에서 나오는 아날로그 데이터를 디지털 신호로 처리하는 A/D변환모듈로 구성되며 생성된 디지털 신호는 USB 인터페이스를 통해 지능정보단말로 전송된다. 지능정 보단말의 촉각 신호처리기에서는 USB 인터페이스를 통해 수신한 디지털 신호를 사용자 인 터페이스를 위한 정보 신호로 처리하여 응용에게 전달한다. 3.6.1 센서 손가락이나 손목의 움직임을 포착하여 아날로그 신호로 전송하는 부분으로 손 관절의 움직 임을 인식하는 Optic 센서와 손목의 회전도와 기울기를 감지하는 Tilt센서로 구성되어 있다. (1) Fiber Optic 센서 엄지를 포함한 각 손가락에 센서를 부착하여 총 5개의 센서를 부착한다. 각 센서는 Fiber optic센서로 만들어졌으며 각 손가락의 펴짐과 구부러짐의 굴곡 정도에 다 른 감지 신호를 발생한다. 샘플당 정확도는 8비트로 256이며 초당 200 샘플을 감지한다. (2) Tilt 센서 경사도와 회전도를 감지하는 센서로서 손목의 기울림 또는 회전정도를 감지한다. 감지된 신호로서 손목의 회전 정도를 기본으로 한 손 동작의 자유도(DOF : Degree of Freedom)를 측정할 수 있다. 이 센서는 60~60도의 선형 구간으로 된 회전도를 측정 참고문헌 1 In the section of ”Emerging technologies” of ACM SIGGRAPH Full Conference 2006. 2 Dietz P. and Leigh D. ”DiamondTouch: A Multi-User Touch Technology.” In Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology (Orlando Florida November11-14 2001).ACM Press NewYork NY pp.219-226 2001. 3 Rekimoto J. “SmartSkin: An Infrastructure for Freehand Manipulation on Interactive Surfaces.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI ’02. ACM Press New York NY pp. 113-120 2002 4 K. Oka Y. Sato and H. Koike ”Real-Time Tracking of Multiple Fingertips and Gesture Recognition for Augmented Desk Interface Systems ” Proc. IEEE Int’l Conf. Automatic Face and Gesture Recognition (FG 2002) IEEE CS Press pp. 429-434 2002. 5 J. Segen and S. Kumar Shadow Gestures: 3D Hand Pose Estimation Using a Single Camera CVPR99 vol. 1 pp. 479-485 Fort Collins Colorado June 23-25 1999. 6 J. Lampinen and E. Oja Distortion Tolerant Pattern Recognition Based on Self-Organizing Feature Extraction IEEE Trans. On Neural Networks vol. 6 no. 3 pp. 539547 May 1995 7 K. C Yow and R. Cipolla Feature-Based Human Face Detection Image and Vision Computing vol. 15 no. 9 pp. 713-735 1997 [노턴의 컴퓨터개론, 정보사회와 컴퓨터] 노턴의 컴퓨터개론 문제와 답 (1장~14장)[원격교육][원격교육사례]원격교육의 개념과 특성, 원격교육의 구성요소, 원격교육의 중요요인, 원격교육의 발달, 원격교육 활성화를 위한 CAL의 활용, 국내외 원격교육 사례, 원격교육 관련 시사점 분석(원격교육)[평생교육개론] 사이버교육- e-learning[교육사회학] 사이버 교육(인터넷 교육)[경영학 인수합병] M&A 사례(인수합병사례)[정보통신] 휴대폰 마케팅 전략 및 사례분석(상)[사이버교육] 사이버 교육(인터넷 교육)의 필요성과 구축절차 및 국내․외 사례를 통해 본 향후 과제 분석[교육] 유비쿼터스시대 미래지향적인 학교교육 내실화 방안[교육방법] 멀티미디어와 원격교육디지털 교육[e러닝][e-learning][이러닝]e러닝(e-learning, 이러닝)의 개념과 발전과정, e러닝(e-learning, 이러닝)의 본질과 정체성, e러닝(e-learning, 이러닝)의 분야별 현황과 활용 기술 사례, e러닝(e-learning, 이러닝)의 정책방향과 시장전망 분석스마트폰 업계의 뜨거운 감자 아이폰 집중 분석 -아이폰(iPhone)의 기능 및 특징과 마케팅 전략, 국내 휴대폰 산업에 미치는 영향 등아이폰 소개(장단점,특징,성능 등)와 아이폰의 성공 원인 및 휴대폰 산업에 미치는 영향 분석, 국내 스마트폰 시장의 해결과제 고찰이력서&자소서 면접족보 조회수:671회
          Nvidia and MIT get a step closer to 'Computer, enhance' image cleaning      Cache   Translate Page   Web Page Cache   
Researchers are training neural network to clean images without seeing the original.
           Nvidia's new AI takes a one-stop approach to fixing grainy photos       Cache   Translate Page   Web Page Cache   

The Noise2Noise AI image-enhancing technology was developed by researchers from NVIDIA, MIT and Finland’s Aalto University#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

If you've ever taken a photo in low light you've probably encountered the grainy effect that can dilute the finished product. A new AI tool could prove an incredibly easy way to remove this so-called noise, with the ability to automatically produce a clean image after analyzing only the corrupted version.

.. Continue Reading Nvidia's new AI takes a one-stop approach to fixing grainy photos

Category: Digital Cameras

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          PCL: Proposal Cluster Learning for Weakly Supervised Object Detection. (arXiv:1807.03342v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Peng Tang, Xinggang Wang, Song Bai, Wei Shen, Xiang Bai, Wenyu Liu, Alan Yuille

Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel end-to-end deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object. This prevents the network from concentrating too much on parts of objects instead of whole objects. We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one. Experiments are conducted on the PASCAL VOC and ImageNet detection benchmarks for WSOD. Results show that our method outperforms the previous state of the art significantly.


          Complex Fully Convolutional Neural Networks for MR Image Reconstruction. (arXiv:1807.03343v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter

Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network ($\mathbb{C}$DFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. $\mathbb{C}$DFNet leverages the inherently complex-valued nature of input k-space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through $\mathbb{C}$DFNet in contrast to its real-valued counterparts.


          What is Deep Learning | Deep Learning and its Applications | Deep Learning Tutorial Video - ExcelR      Cache   Translate Page   Web Page Cache   
ExcelR : Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Things you will learn in this video 1)What is deep learning? 2)Deep learning and its applications 3)Which algorithm behind deep learning? 4)How Neural network will work? To buy eLearning course on Data Science click here https://goo.gl/oMiQMw To register for classroom training click here https://goo.gl/UyU2ve To Enroll for virtual online training click here " https://goo.gl/JTkWXo" SUBSCRIBE HERE for more updates: https://goo.gl/WKNNPx For Introduction to Deep Learning Tutorial click here Deep Learning Tutorial For Beginners For Artificial Neural Network Tutorial click here https://goo.gl/a5tAjn #ExcelRSolutions #DeepLearning #Applicationsofdeeplearning #NeuralNetwork #R-programming #DataScienceCertification #DataSciencetutorial #DataScienceforbeginners #DataScienceTraining ----- For More Information: Toll Free (IND) : 1800 212 2120 | +91 80080 09706 Malaysia: 60 11 3799 1378 USA: 001-844-392-3571 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: enquiry@excelr.com Web: www.excelr.com Connect with us: Facebook: https://www.facebook.com/ExcelR/ LinkedIn: https://www.linkedin.com/company/exce... Twitter: https://twitter.com/ExcelrS G+: https://plus.google.com/+ExcelRSolutions
          Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration. (arXiv:1807.03361v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.


          An Attention Model for group-level emotion recognition. (arXiv:1807.03380v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Aarush Gupta (1), Dakshit Agrawal (1), Hardik Chauhan (1), Jose Dolz (2), Marco Pedersoli (2) ((1) Indian Institute of Technology Roorkee, India, (2) École de Technologie Supérieure, Montreal, Canada)

In this paper we propose a new approach for classifying the global emotion of images containing groups of people. To achieve this task, we consider two different and complementary sources of information: i) a global representation of the entire image (ii) a local representation where only faces are considered. While the global representation of the image is learned with a convolutional neural network (CNN), the local representation is obtained by merging face features through an attention mechanism. The two representations are first learned independently with two separate CNN branches and then fused through concatenation in order to obtain the final group-emotion classifier. For our submission to the EmotiW 2018 group-level emotion recognition challenge, we combine several variations of the proposed model into an ensemble, obtaining a final accuracy of 64.83% on the test set and ranking 4th among all challenge participants.


          On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition. (arXiv:1807.03396v1 [cs.CL])      Cache   Translate Page   Web Page Cache   

Authors: Hao Tang, James Glass

Recurrent neural networks have been the dominant models for many speech and language processing tasks. However, we understand little about the behavior and the class of functions recurrent networks can realize. Moreover, the heuristics used during training complicate the analyses. In this paper, we study recurrent networks' ability to learn long-term dependency in the context of speech recognition. We consider two decoding approaches, online and batch decoding, and show the classes of functions to which the decoding approaches correspond. We then draw a connection between batch decoding and a popular training approach for recurrent networks, truncated backpropagation through time. Changing the decoding approach restricts the amount of past history recurrent networks can use for prediction, allowing us to analyze their ability to remember. Empirically, we utilize long-term dependency in subphonetic states, phonemes, and words, and show how the design decisions, such as the decoding approach, lookahead, context frames, and consecutive prediction, characterize the behavior of recurrent networks. Finally, we draw a connection between Markov processes and vanishing gradients. These results have implications for studying the long-term dependency in speech data and how these properties are learned by recurrent networks.


          IGLOO: Slicing the Features Space to Represent Long Sequences. (arXiv:1807.03402v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Vsevolod Sourkov

We introduce a new neural network architecture, IGLOO, which aims at providing a representation for long sequences where RNNs fail to converge. The structure uses the relationships between random patches sliced out of the features space of some backbone 1 dimensional CNN to find a representation. This paper explains the implementation of the method and provides benchmark results commonly used for RNNs and compare IGLOO to other structures recently published. It is found that IGLOO can deal with sequences of up to 25,000 time steps. For shorter sequences it is also found to be effective and we find that it achieves the highest score in the literature for the permuted MNIST task. Benchmarks also show that IGLOO can run at the speed of the CuDNN optimised GRU or LSTM without being tied to any specific hardware.


          Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks. (arXiv:1807.03404v1 [cond-mat.mtrl-sci])      Cache   Translate Page   Web Page Cache   

Authors: Tian Xie, Jeffrey C. Grossman

The combination of high throughput computation and machine learning has led to a new paradigm in materials design by allowing for the direct screening of vast portions of structural, chemical, and property space. The use of these powerful techniques leads to the generation of enormous amounts of data, which in turn calls for new techniques to efficiently explore and visualize the materials space to help identify underlying patterns. In this work, we develop a unified framework to hierarchically visualize the compositional and structural similarities between materials in an arbitrary material space. We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both. For perovskites, elemental similarities are learned that reflects multiple aspects of atom properties. For elemental boron, structural motifs emerge automatically showing characteristic boron local environments. For inorganic crystals, the similarity and stability of local coordination environments are shown combining different center and neighbor atoms. The method could help transition to a data-centered exploration of materials space in automated materials design.


          Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals. (arXiv:1807.03418v1 [cs.SD])      Cache   Translate Page   Web Page Cache   

Authors: Sören Becker, Marcel Ackermann, Sebastian Lapuschkin, Klaus-Robert Müller, Wojciech Samek

Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. In this paper, two neural network architectures are trained on spectrogram and raw waveform data for audio classification tasks on a newly created audio dataset and layer-wise relevance propagation (LRP), a previously proposed interpretability method, is applied to investigate the models' feature selection and decision making. It is demonstrated that the networks are highly reliant on feature marked as relevant by LRP through systematic manipulation of the input data. Our results show that by making deep audio classifiers interpretable, one can analyze and compare the properties and strategies of different models beyond classification accuracy, which potentially opens up new ways for model improvements.


          Predicting property damage from tornadoes with deep learning. (arXiv:1807.03456v1 [stat.ML])      Cache   Translate Page   Web Page Cache   

Authors: Jeremy Diaz, Maxwell Joseph

Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations in tornado-prone areas continue to increase in density and extent. Here, we use artificial neural networks to predict tornado-induced property damage using publicly available data. We find that the large number of tornadoes which cause zero property damage (30.6% of the data) poses a challenge for predictive models. We developed a model that predicts whether a tornado will cause property damage to a high degree of accuracy (out of sample accuracy = 0.829 and AUROC = 0.873). Conditional on a tornado causing damage, another model predicts the amount of damage. When combined, these two models yield an expected value for the amount of property damage caused by a tornado event. From the best-performing models (out of sample mean squared error = 0.089 and R2 = 0.473), we provide an interactive, gridded map of monthly expected values for the year 2018. One major weakness is that the model predictive power is optimized with log-transformed, mean-normalized property damages, however this leads to large natural-scale residuals for the most destructive tornadoes. The predictive capacity of this model along with an interactive interface may provide an opportunity for science-informed tornado disaster planning.


          SceneEDNet: A Deep Learning Approach for Scene Flow Estimation. (arXiv:1807.03464v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Ravi Kumar Thakur, Snehasis Mukherjee

Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics. The state-of-the-art techniques for scene flow estimation, typically rely on the knowledge of scene structure of the frame and the correspondence between frames. However, with the increasing amount of RGB-D data captured from sophisticated sensors like Microsoft Kinect, and the recent advances in the area of sophisticated deep learning techniques, introduction of an efficient deep learning technique for scene flow estimation, is becoming important. This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture. The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images. The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth. The proposed architecture is applied on a huge dataset and provides meaningful results.


          Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates. (arXiv:1807.03470v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Jianrui Cai, Zisheng Cao, Lei Zhang

Lossy image compression (LIC), which aims to utilize inexact approximations to represent an image more compactly, is a classical problem in image processing. Recently, deep convolutional neural networks (CNNs) have achieved interesting results in LIC by learning an encoder-quantizer-decoder network from a large amount of data. However, existing CNN-based LIC methods usually can only train a network for a specific bits-per-pixel (bpp). Such a "one network per bpp" problem limits the generality and flexibility of CNNs to practical LIC applications. In this paper, we propose to learn a single CNN which can perform LIC at multiple bpp rates. A simple yet effective Tucker Decomposition Network (TDNet) is developed, where there is a novel tucker decomposition layer (TDL) to decompose a latent image representation into a set of projection matrices and a core tensor. By changing the rank of the core tensor and its quantization, we can easily adjust the bpp rate of latent image representation within a single CNN. Furthermore, an iterative non-uniform quantization scheme is presented to optimize the quantizer, and a coarse-to-fine training strategy is introduced to reconstruct the decompressed images. Extensive experiments demonstrate the state-of-the-art compression performance of TDNet in terms of both PSNR and MS-SSIM indices.


          Phase reconstruction from amplitude spectrograms based on von-Mises-distribution deep neural network. (arXiv:1807.03474v1 [cs.SD])      Cache   Translate Page   Web Page Cache   

Authors: Shinnosuke Takamichi, Yuki Saito, Norihiro Takamune, Daichi Kitamura, Hiroshi Saruwatari

This paper presents a deep neural network (DNN)-based phase reconstruction from amplitude spectrograms. In audio signal and speech processing, the amplitude spectrogram is often used for processing, and the corresponding phase spectrogram is reconstructed from the amplitude spectrogram on the basis of the Griffin-Lim method. However, the Griffin-Lim method causes unnatural artifacts in synthetic speech. Addressing this problem, we introduce the von-Mises-distribution DNN for phase reconstruction. The DNN is a generative model having the von Mises distribution that can model distributions of a periodic variable such as a phase, and the model parameters of the DNN are estimated on the basis of the maximum likelihood criterion. Furthermore, we propose a group-delay loss for DNN training to make the predicted group delay close to a natural group delay. The experimental results demonstrate that 1) the trained DNN can predict group delay accurately more than phases themselves, and 2) our phase reconstruction methods achieve better speech quality than the conventional Griffin-Lim method.


          An Adaptive Learning Method of Restricted Boltzmann Machine by Neuron Generation and Annihilation Algorithm. (arXiv:1807.03478v1 [cs.NE])      Cache   Translate Page   Web Page Cache   

Authors: Shin Kamada, Takumi Ichimura

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper.


          Automatic Rumor Detection on Microblogs: A Survey. (arXiv:1807.03505v1 [cs.SI])      Cache   Translate Page   Web Page Cache   

Authors: Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, Jintao Li

The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.


          Deep Underwater Image Enhancement. (arXiv:1807.03528v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Saeed Anwar, Chongyi Li, Fatih Porikli

In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network.


          A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees. (arXiv:1807.03571v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations. We demonstrate that, under the assumption of Lipschitz continuity, both problems can be approximated using finite optimisation by discretising the input space, and the approximation has provable guarantees, i.e., the error is bounded. We then show that the resulting optimisation problems can be reduced to the solution of two-player turn-based games, where the first player selects features and the second perturbs the image within the feature. While the second player aims to minimise the distance to an adversarial example, depending on the optimisation objective the first player can be cooperative or competitive. We employ an anytime approach to solve the games, in the sense of approximating the value of a game by monotonically improving its upper and lower bounds. The Monte Carlo tree search algorithm is applied to compute upper bounds for both games, and the Admissible A* and the Alpha-Beta Pruning algorithms are, respectively, used to compute lower bounds for the maximum safety radius and feature robustness games. When working on the upper bound of the maximum safe radius problem, our tool demonstrates competitive performance against existing adversarial example crafting algorithms. Furthermore, we show how our framework can be deployed to evaluate pointwise robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.


          Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images. (arXiv:1807.03613v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li

Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, including feature extraction, informative feature selection, and final pixel classification, are commonly used in these traditional methods. Therefore, the final classification accuracy can be jeopardized by error or inaccuracy within each of these steps. In this study, we proposed a convolutional neural network (CNN) based method to automatically characterize plaques in OCT images. Unlike traditional methods, our method uses the image as a direct input and performs classification as a single-step process. The experiments on 269 OCT images showed that the average prediction accuracy of CNN-based method was 0.866, which indicated a great promise for clinical translation.


          Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks. (arXiv:1807.03651v1 [cs.CV])      Cache   Translate Page   Web Page Cache   

Authors: Omer Rajput, Nils Gessert, Martin Gromniak, Lars Matthäus, Alexander Schlaefer

Head pose estimation and tracking is useful in variety of medical applications. With the advent of RGBD cameras like Kinect, it has become feasible to do markerless tracking by estimating the head pose directly from the point clouds. One specific medical application is robot assisted transcranial magnetic stimulation (TMS) where any patient motion is compensated with the help of a robot. For increased patient comfort, it is important to track the head without markers. In this regard, we address the head pose estimation problem using two different approaches. In the first approach, we build upon the more traditional approach of model based head tracking, where a head model is morphed according to the particular head to be tracked and the morphed model is used to track the head in the point cloud streams. In the second approach, we propose a new multi-scale convolutional neural network architecture for more accurate pose regression. Additionally, we outline a systematic data set acquisition strategy using a head phantom mounted on the robot and ground-truth labels generated using a highly accurate tracking system.


          Deep Learning on Low-Resource Datasets. (arXiv:1807.03697v1 [cs.LG])      Cache   Translate Page   Web Page Cache   

Authors: Veronica Morfi, Dan Stowell

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.


          Convolutional Normalizing Flows. (arXiv:1711.02255v2 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Guoqing Zheng, Yiming Yang, Jaime Carbonell

Bayesian posterior inference is prevalent in various machine learning problems. Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation. Recently, there has a trend of using neural networks to approximate the variational posterior distribution due to the flexibility of neural network architecture. One way to construct flexible variational distribution is to warp a simple density into a complex by normalizing flows, where the resulting density can be analytically evaluated. However, there is a trade-off between the flexibility of normalizing flow and computation cost for efficient transformation. In this paper, we propose a simple yet effective architecture of normalizing flows, ConvFlow, based on convolution over the dimensions of random input vector. Experiments on synthetic and real world posterior inference problems demonstrate the effectiveness and efficiency of the proposed method.


          Asymmetric Variational Autoencoders. (arXiv:1711.08352v2 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Guoqing Zheng, Yiming Yang, Jaime Carbonell

Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower Bound (ELBO) of the true data likelihood with respect to a variational distribution. However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables. In this paper, we propose a novel framework to enrich the variational family by incorporating auxiliary variables to the variational family. The resulting inference network doesn't require density evaluations for the auxiliary variables and thus complex implicit densities over the auxiliary variables can be constructed by neural networks. It can be shown that the actual variational posterior of the proposed approach is essentially modeling a rich probabilistic mixture of simple variational posterior indexed by auxiliary variables, thus a flexible inference model can be built. Empirical evaluations on several density estimation tasks demonstrates the effectiveness of the proposed method.


           Nvidia's new AI takes a one-stop approach to fixing grainy photos       Cache   Translate Page   Web Page Cache   

The Noise2Noise AI image-enhancing technology was developed by researchers from NVIDIA, MIT and Finland’s Aalto University#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

If you've ever taken a photo in low light you've probably encountered the grainy effect that can dilute the finished product. A new AI tool could prove an incredibly easy way to remove this so-called noise, with the ability to automatically produce a clean image after analyzing only the corrupted version.

.. Continue Reading Nvidia's new AI takes a one-stop approach to fixing grainy photos

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          Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning. (arXiv:1801.02265v4 [cs.CR] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Payap Sirinam, Mohsen Imani, Marc Juarez, Matthew Wright

Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting attacks have been shown to be effective even against Tor. Recently, lightweight website fingerprinting defenses for Tor have been proposed that substantially degrade existing attacks: WTF-PAD and Walkie-Talkie. In this work, we present Deep Fingerprinting (DF), a new website fingerprinting attack against Tor that leverages a type of deep learning called convolution neural networks (CNN) with a sophisticated architecture design, and we evaluate this attack against WTF-PAD and Walkie-Talkie. The DF attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also the only attack that is effective against WTF-PAD with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to just 49.7% accuracy. In the more realistic open-world setting, our attack remains effective, with 0.99 precision and 0.94 recall on undefended traffic. Against traffic defended with WTF-PAD in this setting, the attack still can get 0.96 precision and 0.68 recall. These findings highlight the need for effective defenses that protect against this new attack and that could be deployed in Tor.


          Towards Arbitrary Noise Augmentation - Deep Learning for Sampling from Arbitrary Probability Distributions. (arXiv:1801.04211v2 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Felix Horger, Tobias Würfl, Vincent Christlein, Andreas Maier

Accurate noise modelling is important for training of deep learning reconstruction algorithms. While noise models are well known for traditional imaging techniques, the noise distribution of a novel sensor may be difficult to determine a priori. Therefore, we propose learning arbitrary noise distributions. To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function. During the training, the Jensen-Shannon divergence between the distribution of the model's output and the target distribution is minimized. We experimentally demonstrate that our model converges towards the desired state. It provides an alternative to existing sampling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo. Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.


          Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks. (arXiv:1802.07486v4 [physics.comp-ph] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Pantelis R. Vlachas, Wonmin Byeon, Zhong Y. Wan, Themistoklis P. Sapsis, Petros Koumoutsakos

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.


          A Compact Network Learning Model for Distribution Regression. (arXiv:1804.04775v3 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Connie Kou, Hwee Kuan Lee, Teck Khim Ng

Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Our proposed Distribution Regression Network (DRN) achieves higher prediction accuracies while being much more compact and uses fewer parameters than traditional neural networks.


          Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components. (arXiv:1804.06760v2 [cs.SY] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Cumhur Erkan Tuncali, Georgios Fainekos, Hisahiro Ito, James Kapinski

Many organizations are developing autonomous driving systems, which are expected to be deployed at a large scale in the near future. Despite this, there is a lack of agreement on appropriate methods to test, debug, and certify the performance of these systems. One of the main challenges is that many autonomous driving systems have machine learning components, such as deep neural networks, for which formal properties are difficult to characterize. We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems. We demonstrate how the framework can be used to evaluate closed-loop properties of an autonomous driving system model that includes the ML components, all within a virtual environment. We demonstrate how to use test case generation methods, such as covering arrays, as well as requirement falsification methods to automatically identify problematic test scenarios. The resulting framework can be used to increase the reliability of autonomous driving systems.


          A Unified Particle-Optimization Framework for Scalable Bayesian Sampling. (arXiv:1805.11659v2 [stat.ML] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis. A standard SG-MCMC algorithm simulates samples from a discrete-time Markov chain to approximate a target distribution, thus samples could be highly correlated, an undesired property for SG-MCMC. In contrary, SVGD directly optimizes a set of particles to approximate a target distribution, and thus is able to obtain good approximations with relatively much fewer samples. In this paper, we propose a principle particle-optimization framework based on Wasserstein gradient flows to unify SG-MCMC and SVGD, and to allow new algorithms to be developed. Our framework interprets SG-MCMC as particle optimization on the space of probability measures, revealing a strong connection between SG-MCMC and SVGD. The key component of our framework is several particle-approximate techniques to efficiently solve the original partial differential equations on the space of probability measures. Extensive experiments on both synthetic data and deep neural networks demonstrate the effectiveness and efficiency of our framework for scalable Bayesian sampling.


          Semi-Supervised Clustering with Neural Networks. (arXiv:1806.01547v2 [cs.LG] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Ankita Shukla, Gullal Singh Cheema, Saket Anand

Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (<5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and state of the art deep clustering approaches.


          Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks. (arXiv:1806.05393v2 [stat.ML] UPDATED)      Cache   Translate Page   Web Page Cache   

Authors: Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington

In recent years, state-of-the-art methods in computer vision have utilized increasingly deep convolutional neural network architectures (CNNs), with some of the most successful models employing hundreds or even thousands of layers. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging. While residual connections and batch normalization do enable training at these depths, it has remained unclear whether such specialized architecture designs are truly necessary to train deep CNNs. In this work, we demonstrate that it is possible to train vanilla CNNs with ten thousand layers or more simply by using an appropriate initialization scheme. We derive this initialization scheme theoretically by developing a mean field theory for signal propagation and by characterizing the conditions for dynamical isometry, the equilibration of singular values of the input-output Jacobian matrix. These conditions require that the convolution operator be an orthogonal transformation in the sense that it is norm-preserving. We present an algorithm for generating such random initial orthogonal convolution kernels and demonstrate empirically that they enable efficient training of extremely deep architectures.


           Fuzzy rationality and utility theory axioms.       Cache   Translate Page   Web Page Cache   
Cutello, V. y Montero, Javier (1999) Fuzzy rationality and utility theory axioms. In 18th International Conference of the North American Fuzzy Information Processing Society NAFIPS : june 10-12, 1999, New York, USA / edited by Rajesh N. Davé, Thomas Sudkamp ; sponsored by NAFIPS in cooperation with IEEE Neural Networks Council and IEEE Sy. IEEE, New York, NY, 332 -336. ISBN 0-7803-5211-4
           On the principles of fuzzy classification.       Cache   Translate Page   Web Page Cache   
Amo, Ana del y Montero, Javier (1999) On the principles of fuzzy classification. In 18th International Conference of the North American Fuzzy Information Processing Society NAFIPS : june 10-12, 1999, New York, USA / edited by Rajesh N. Davé, Thomas Sudkamp ; sponsored by NAFIPS in cooperation with IEEE Neural Networks Council and IEEE Sy. IEEE, New York, NY, pp. 675-679. ISBN 0-7803-5211-4
          Breakthrough in construction of computers for mimicking human brain      Cache   Translate Page   Web Page Cache   
(Frontiers) A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers, with the aim of advancing our knowledge of neural processing in the brain, including learning and disorders such as epilepsy and Alzheimer's disease.
          Scientifique de données en mégadonnées - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Maîtrise des techniques analytiques appliquées (clustering, decision trees, neural networks, SVM (support vector machines), collaborative filtering, k-nearest...
From Intact - Sat, 30 Jun 2018 18:58:20 GMT - View all Montréal, QC jobs
          Breakthrough in construction of computers for mimicking human brain      Cache   Translate Page   Web Page Cache   
(Frontiers) A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers, with the aim of advancing our knowledge of neural processing in the brain, including learning and disorders such as epilepsy and Alzheimer's disease. (Source: EurekAlert! - Medicine and Health)

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          Breakthrough in construction of computers for mimicking human brain      Cache   Translate Page   Web Page Cache   
A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers, with the aim of advancing our knowledge of neural processing in the brain, including learning and disorders such as epilepsy and Alzheimer's disease.
          Non-Convex Multi-species Hopfield models. (arXiv:1807.03609v1 [cond-mat.dis-nn])      Cache   Translate Page   Web Page Cache   

Authors: Elena Agliari, Danila Migliozzi, Daniele Tantari

In this work we introduce a multi-species generalization of the Hopfield model for associative memory, where neurons are divided into groups and both inter-groups and intra-groups pair-wise interactions are considered, with different intensities. Thus, this system contains two of the main ingredients of modern Deep neural network architectures: Hebbian interactions to store patterns of information and multiple layers coding different levels of correlations. The model is completely solvable in the low-load regime with a suitable generalization of the Hamilton-Jacobi technique, despite the Hamiltonian can be a non-definite quadratic form of the magnetizations. The family of multi-species Hopfield model includes, as special cases, the 3-layers Restricted Boltzmann Machine (RBM) with Gaussian hidden layer and the Bidirectional Associative Memory (BAM) model.


          A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B-Spline Curve with Knot Insertion Algorithm      Cache   Translate Page   Web Page Cache   
Tide levels depend on both long-term astronomical effects that are mainly affected by moon and sun and short-term meteorological effects generated by severe weather conditions like storm surge. Storm surge caused by typhoons will impose serious security risks and threats on the coastal residents’ safety in production, property, and life. Due to the challenges of nonperiodic and incontinuous tidal level record data and the influence of multimeteorological factors, the existing methods cannot predict the tide levels affected by typhoons precisely. This paper targets to explore a more advanced method for forecasting the tide levels of storm surge caused by typhoons. First, on the basis of successive five-year tide level and typhoon data at Luchaogang, China, a BP neural network model is developed using six parameters of typhoons as input parameters and the relevant tide level data as output parameters. Then, for an improved forecasting accuracy, cubic B-spline curve with knot insertion algorithm is combined with the BP model to conduct smooth processing of the predicted points and thus the smoothed prediction curve of tidal level has been obtained. By using the data of the fifth year as the testing sample, the predicted results by the two methods are compared. The experimental results have shown that the latter approach has higher accuracy in forecasting tidal level of storm surge caused by typhoons, and the combined prediction approach provides a powerful tool for defending and reducing storm surge disaster.
          Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance      Cache   Translate Page   Web Page Cache   
Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.
          Unit4 Delivers Enterprise Scale AI-Powered Performance Management      Cache   Translate Page   Web Page Cache   
...to drive new levels of data-driven decision making for forward-looking organizations. Boards today expect high quality analyses, suggestions, forecasts and budgets and they want them faster and more frequently. Prevero with AI takes advantage of neural networks and deep ...


          AI Senior Analyst      Cache   Translate Page   Web Page Cache   
Techno-Functional analyst who have hands-on experience in NLP (Natural Language Processing) ML (Machine Learning) Speech Recognition Neural Networks and Deep Learning coding (more) p Login for more job information and to Apply
          Neural circuits for long-range color filling-in.      Cache   Translate Page   Web Page Cache   
Related Articles

Neural circuits for long-range color filling-in.

Neuroimage. 2018 Jul 03;181:30-43

Authors: Gerardin P, Abbatecola C, Devinck F, Kennedy H, Dojat M, Knoblauch K

Abstract
Surface color appearance depends on both local surface chromaticity and global context. How are these inter-dependencies supported by cortical networks? Combining functional imaging and psychophysics, we examined if color from long-range filling-in engages distinct pathways from responses caused by a field of uniform chromaticity. We find that color from filling-in is best classified and best correlated with appearance by two dorsal areas, V3A and V3B/KO. In contrast, a field of uniform chromaticity is best classified by ventral areas hV4 and LO. Dynamic causal modeling revealed feedback modulation from area V3A to areas V1 and LO for filling-in, contrasting with feedback from LO modulating areas V1 and V3A for a matched uniform chromaticity. These results indicate a dorsal stream role in color filling-in via feedback modulation of area V1 coupled with a cross-stream modulation of ventral areas suggesting that local and contextual influences on color appearance engage distinct neural networks.

PMID: 29986833 [PubMed - as supplied by publisher]


          Facebook is teaching AI to talk you through directions      Cache   Translate Page   Web Page Cache   

Facebook is developing a “Talk the Walk” AI capable of giving walking directions without knowing a user’s location. What it is: A team comprised of a researcher from the University of Montreal in Canada and Facebook Artificial Intelligence Research (FAIR) scientists recently published a white paper describing a neural network capable of giving a person plain language directions without the use of GPS or other location tracking aids. According to the researchers: We introduce the Talk the Walk dataset, where the aim is for two agents, a “guide” and a “tourist”, to interact with each other via natural language in order…

This story continues at The Next Web

Or just read more coverage about: Facebook
          proof read an Engineering, computer science technical report on face detection -- 2      Cache   Translate Page   Web Page Cache   
I am looking for a high quality researcher in computer science and vision who has scientific knowledge in deep neural networks and machine learning ..and local features on facial detection and also do... (Budget: $30 - $250 USD, Jobs: Electrical Engineering, Machine Learning, Neural Networks, Proofreading, Python)
           Effects of spike anticipation on the spiking dynamics of neural networks       Cache   Translate Page   Web Page Cache   
Santos Sierra, Daniel de y Sánchez Jiménez, Abel y García-Vellisca, A. y Navas, Adrian y Villacorta Atienza, Jose A. (2015) Effects of spike anticipation on the spiking dynamics of neural networks. Frontiers in Computational Neuroscience, 9 . pp. 1-10. ISSN ESSN: 1662-5188
          Breakthrough in construction of computers for mimicking human brain      Cache   Translate Page   Web Page Cache   
A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers, with the aim of advancing our knowledge of neural processing in the brain, including learning and disorders such as epilepsy and Alzheimer's disease.
          Comment on How to Develop an N-gram Multichannel Convolutional Neural Network for Sentiment Analysis by Yuval      Cache   Translate Page   Web Page Cache   
I plotted precision-recall curve for both pos and neg class and found the results interesting, While the curve for the pos class looks very good with oprtimal point at 0.9 and 0.8 for recall and precision respecitvally. The curve for the neg class is a stright line that gives 0.6 for precision and recall or 0.8 with 0.6 and vice versa. Any idea how this could be?
          Comment on How to Grid Search Hyperparameters for Deep Learning Models in Python With Keras by Kemas Farosi      Cache   Translate Page   Web Page Cache   
Hi Jason, Great tutorial, I have a question, is it possible to find how many hidden layers in my deep neural networks by grid search ? because i want to find the best layer numbers in my DNN. thanks
          Facebook is teaching AI to talk you through directions      Cache   Translate Page   Web Page Cache   

Facebook is developing a “Talk the Walk” AI capable of giving walking directions without knowing a user’s location. What it is: A team comprised of a researcher from the University of Montreal in Canada and Facebook Artificial Intelligence Research (FAIR) scientists recently published a white paper describing a neural network capable of giving a person plain language directions without the use of GPS or other location tracking aids. According to the researchers: We introduce the Talk the Walk dataset, where the aim is for two agents, a “guide” and a “tourist”, to interact with each other via natural language in order…

This story continues at The Next Web

Or just read more coverage about: Facebook

          TensorFlow for Neural Network Solutions      Cache   Translate Page   Web Page Cache   
скачать TensorFlow for Neural Network Solutions бесплатно
Название: TensorFlow for Neural Network Solutions
Автор: Nick McClure
Страниц: Duration: 1h 39m
Формат: HDRip
Размер: 332,9 mb
Качество: Отличное
Язык: Английский
Жанр: Video Course
Год издания: 2018


Unleash the power of TensoeFlow to train efficient neural networks


          NVIDIA introduced AI, capable of cleaning photos from noise      Cache   Translate Page   Web Page Cache   

A team of researchers from NVIDIA, the Massachusetts Institute of Technology and Aalto University introduced a neural network, which is trained to clean up noise-contaminated photographs. Even without ever seeing the source, emphasizes The Next Web . Noise2Noise AI was trained on 50 000 photos, among which were both MRI shots, and computer generated with the addition of […]

The post NVIDIA introduced AI, capable of cleaning photos from noise appeared first on HybridTechCar.


          NVIDIA Uses AI to Banish Noise from Images      Cache   Translate Page   Web Page Cache   
As NVIDIA explains, typical deep learning approaches have required training a neural network to recognize when a clean end state image should look ...
          A deep neural network is being harnessed to analyse nuclear events      Cache   Translate Page   Web Page Cache   
These findings are generated by the utilisation of deep learning in order to teach and program machines and systems to learn and make decisions ...
           L'intelligenza artificiale NVidia corregge le foto affette da rumore digitale       Cache   Translate Page   Web Page Cache   
È capitato praticamente a tutti: scattando una foto in condizioni di luce scarsa l'immagine appare affetta da un evidente problema. Essa appare 'granulosa', rovinata dalla presenza di una miriade di puntini (il cosiddetto 'rumore digitale').
Nelle situazioni peggiori diventa addirittura difficoltoso riconoscere il soggetto ritratto nella foto.

I tecnici di NVidia, di concerto con i ricercatori del MIT e gli accademici della Aalto University hanno messo a punto un sistema chiamato Noise2Noise in grado di restaurare l'immagine rimuovendo ogni traccia del rumore digitale.

Per raggiungere l'ambizioso traguardo i ricercatori hanno usato una batteria di GPU NVidia Tesla P100 e il framework per il deep learning TensorFlow accelerato mediante l'utilizzo delle librerie cuDNN (CUDA Deep Neural Network), esattamente come fatto ad aprile scorso: NVidia ricostruisce le immagini danneggiate grazie all'intelligenza artificiale.


Addestrando la rete neurale con oltre 50.000 immagini provenienti dai database ImageNet (la base dati contiene sia l'immagine affetta dal rumore digitale che la versione esente da difetti), l'intelligenza artificiale così messa a punto è stata poi in grado di correggere anche le foto più problematiche.

Noise2Noise è stato in grado di riconoscere i soggetti ritratti in ciascuna immagine e di agire di conseguenza applicando le correzioni fotografiche migliori.
           Nvidia's new AI takes a one-stop approach to fixing grainy photos       Cache   Translate Page   Web Page Cache   

The Noise2Noise AI image-enhancing technology was developed by researchers from NVIDIA, MIT and Finland’s Aalto University#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

If you've ever taken a photo in low light you've probably encountered the grainy effect that can dilute the finished product. A new AI tool could prove an incredibly easy way to remove this so-called noise, with the ability to automatically produce a clean image after analyzing only the corrupted version.

.. Continue Reading Nvidia's new AI takes a one-stop approach to fixing grainy photos

Category: Digital Cameras

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          DNA-Based Neural Network Identifies Human Handwriting      Cache   Translate Page   Web Page Cache   

Robots are becoming more human every day. Case in point, Caltech researchers developed an artificial neural network made of DNA. Even more impressive, though, is that the AI can solve a classic machine […]

The post DNA-Based Neural Network Identifies Human Handwriting appeared first on Geek.com.


          Data Scientist in big data - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Fluency in applied analytical techniques including regression analysis, clustering, decision trees, neural networks, SVM (support vector machines),...
From Intact - Sun, 08 Apr 2018 05:10:26 GMT - View all Montréal, QC jobs
          Scientifique de données en mégadonnées - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Maîtrise des techniques analytiques appliquées (clustering, decision trees, neural networks, SVM (support vector machines), collaborative filtering, k-nearest...
From Intact - Sat, 30 Jun 2018 18:58:20 GMT - View all Montréal, QC jobs
          Data Scientist in big data - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Fluency in applied analytical techniques including regression analysis, clustering, decision trees, neural networks, SVM (support vector machines),...
From Intact - Sun, 08 Apr 2018 05:10:26 GMT - View all Montréal, QC jobs
          Scientifique de données en mégadonnées - Intact - Montréal, QC      Cache   Translate Page   Web Page Cache   
Maîtrise des techniques analytiques appliquées (clustering, decision trees, neural networks, SVM (support vector machines), collaborative filtering, k-nearest...
From Intact - Sat, 30 Jun 2018 18:58:20 GMT - View all Montréal, QC jobs
          Computing of Temporal Information in Spiking Neural Networks with ReRAM Synapses      Cache   Translate Page   Web Page Cache   
Faraday Discuss., 2018, Accepted Manuscript
DOI: 10.1039/C8FD00097B, Paper
Open Access Open Access
Creative Commons Licence  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
Wei Wang, Giacomo Pedretti, Valerio Milo, Roberto Carboni, Alessandro Calderoni, Nirmal Ramaswamy, Alessandro Spinelli, Daniele Ielmini
Resistive switching random-access memory (ReRAM) is a two-terminal device based on ion migration to induce resistance switching between a high resistance state (HRS) and a low resistance state (LRS). ReRAM...
The content of this RSS Feed (c) The Royal Society of Chemistry

          Neural Network Programming with TensorFlow      Cache   Translate Page   Web Page Cache   



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