He’s an expert on machine learning, and he specializes in deep neural networks in the areas of computer vision and natural language processing. Sort by citations Sort by year Sort by title. Please visit instead my Mila page for up-to-date information about me. Massachusetts Institute of Technology, Arvind Thiagarajan. Few-shot learning is the problem of learning new tasks from little amounts of labeled data. and recommended readings. Vision Concept Segmentation Detection OpenCV. Sparse coding 9. Python Basic & Pandas & Numpy Django Django-RestFramework Crawling Embedded GUI. Sign in Sign up for free; Hugo Larochelle: Neural Networks ML Review July 04, 2017 Research 0 300. Hugo Larochelle redet in “The Deep End of Deep Learning” über den langen Weg, den Deep Learning gehen musste, bis es zum Buzzword wurde. I am the lead of the Google Brain team in Montreal, adjunct professor at Université de Montréal and a Canada CIFAR Chair. Hugo Larochelle Google Brain Slides from CIFAR Deep Learning Summer School. This topic has gained tremendous interest in the past few years, with several new methods being proposed each month. I currently lead the Google Brain group in Montreal. Welcome to … Feedforward neural network 2. Download PDF Abstract: We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Hugo Larochelle. A lot of the recent progress on many AI tasks were enabled in part by the availability of large quantities of labeled data for deep learning. His main area of expertise is in deep learning. Deep learning in breast cancer screening Dinner (18:15-19:15) Dinner (17:45-18:45) Dinner (17:45-18:45) Free time Poster session (19:30-22:00) With snacks and local beer! Box 6128, Succ. Year ; Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. My main area of expertise is deep learning. Introduction and math revision 1. My main area of expertise is deep learning. Articles Cited by Co-authors. Hugo Larochelle Home; Publications; University; Links; French; Recent stuff I am no longer updating this website. Twitter Inc., Hugo Larochelle. LinkedIn. Hugo Larochelle Google Brain Slides from CIFAR Deep Learning Summer School. Meta-learning has been a promising framework for addressing the problem of generalizing from small amounts of data, known as few-shot learning. Sort . Yet, humans are able to learn new concepts or tasks from as little as a handful of examples. Papers discussing non-linear conditional random fields: Precursor paper on conditional random fields: Papers on alternative training methods for conditional random fields: Paper describing different methods for taking into account the test-time error function during training: Other paper on other approaches for training models with intractable normalization constants: Papers on extensions of the restricted Boltzmann machine: Papers on more advanced sampling methods: Theoretical paper demonstrating the optimality results for the linear autoencoder: Papers on different extensions of the autoencoder: Experimental evaluations of deep learning methods: Papers on alternative approaches for unsupervised pre-training of deep networks: Papers on dropout regularisation methods: Paper on another type of non-feedfoward deep network: Papers on other sparse representation models: Method to accelerate inference in sparse coding model: Experimental evaluation of good practices in using convolutional networks: Convolutional version of the restricted Boltzmann machine: Summary of the neurophysiology of the visual cortex: Different applications to computer vision of neural networks: Papers on language modeling with neural networks: Other papers on word tagging with neural networks: Other efficient training algorithms for text data: Papers on learning word vector representations. In this talk, I’ll present an … The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public. Since late summer 2015, he has been drafting and publicly sharing notes on arXiv machine learning papers that he has taken an interest in. My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. Deep Learning Course by CILVR lab @ NYU 5. Motivated by theories of perception, the model consists of two interacting pathways: identity and control, … Twitter Inc., Jeshua Bratman. See All by ML Review . Twitter Inc., Conrado Miranda. Object detection in airport security X-ray scans Poster teasers (17:15-18:00) Free time Short talk. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. Top recent deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field. Speaker Deck. Doina Precup, Research Team Lead at DeepMind shared the latest developments in Reinforcement Learning and how it can be used as a tool for building knowledge bases for AI Agents.... Hollie Jaques 24 October 2019 AI Assistants Taking a Leap … 01. He is a research scientist over at Google Brain. Hugo has 10 jobs listed on their profile. He’s one of the world’s brightest stars in artificial-intelligence research. At the time of this writing he has shared notes on 10 papers. Deep Learning is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of domains (vision, language, speech, reasoning, robotics, AI in general), leading to some pretty significant commercial success and exciting new … See All by ML Review . My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. Today I’m excited; our guest is Hugo Larochelle. More broadly, I’m interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural … Centre-Ville, Montreal, H3C 3J7, Qc, Canada CS231n ETC. Manasi Vartak. Professor: Hugo Larochelle Welcome to my online course on neural networks! He is particularly interested in deep neural networks, mostly applied in the context of big data and to artificial intelligence problems such as computer vision and natural language processing. Hugo has 10 jobs listed on their profile. Hugo Larochelle. July 04, 2017 Tweet Share More Decks by ML Review. Deep Learning using Robust Interdependent Codes Hugo Larochelle, Dumitru Erhan and Pascal Vincent Dept. Massachusetts Institute of Technology, Arvind Thiagarajan. Experimental evaluations of deep learning methods: An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation by Hugo Larochelle, Dumitru … Each week is associated with explanatory video clips Hugo Larochelle, PhD, is a Université de Sherbrooke machine learning professor (on leave), Twitter research scientist, noted neural network researcher, and deep learning aficiando. Deep Learning Tutorial by LISA lab, University of Montreal COURSES 1. Hugo Larochelle is a computer scientist whose research focuses on machine learning, i.e., on the development of algorithms capable of extracting concepts and abstractions from data. Hugo Larochelle, PhD, is a Université de Sherbrooke machine learning professor (on leave), Twitter research scientist, noted neural network researcher, and deep learning aficiando. View Hugo Larochelle’s profile on LinkedIn, the world’s largest professional community. Deep Learning for Computer Vision (Andrej Karpathy, OpenAI) 03. Natural … Sort by citations Sort by year Sort by title. View Hugo Larochelle’s profile on LinkedIn, the world’s largest professional community. Speaker Deck. About. Midterm Review • Polynomial curve fitting – generalization, overfitting • Loss functions for regression • Generalization / Overfitting • Statistical Decision Theory . Hugo Larochelle. Restricted Boltzmann machines. Summary Sentence: Hugo Larochelle currently leads the Google Brain group in Montreal. IRO, Universit´e de Montr´eal P.O. A lot of the recent progress on many AI tasks were enabled in part by the availability of large quantities of labeled data for deep learning. Restricted Boltzmann Machines in Shark [UPDATE 15/08] Installation instructions … Hugo Larochelle shares his observations of what’s been made possible with the underpinnings of Deep Learning. segmented over 10 weeks. Yet, humans are able to learn new concepts or tasks from as little as a handful of examples. Deep Learning Day at KDD 2020. 09/04/2020 ∙ by Mohammad Fasha ∙ 144 learn2learn: A Library for Meta-Learning Research. Download PDF Abstract: We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Machine Learning Artificial Intelligence. Motivated by theories of perception, the model consists of two interacting pathways: identity and control, … ML Review. %0 Conference Paper %T Efficient Learning of Deep Boltzmann Machines %A Ruslan Salakhutdinov %A Hugo Larochelle %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-salakhutdinov10a %I PMLR %J Proceedings of Machine Learning … Twitter Inc., Jeshua Bratman. Deep Belief Network 7 • Deep Belief Networks: Ø it is a generative model that mixes undirected and directed connections between variables Ø top 2 layers’ distribution is an RBM! Top recent deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field. Hugo Larochelle | DeepAI Associate Director - Learning in Machines and Brains Program at Canadian Institute for Advanced Research, Adjunct Professor at Université de Sherbrooke, Adjunct Professor at Université de Montréal, Research Scientist at Google ///countCtrl.countPageResults("of")/// publications. IRO, Universit´e de Montr´eal P.O. Pattern Analysis and Machine Intelligence | August 2013, Vol 35 Download BibTex . Conditional random fields. Hugo Larochelle is Research Scientist at Twitter Cortex, and Assistant Professor at the Université de Sherbrooke.Prior to this, he spent two years in the Machine Learning Group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton, and obtained his PhD at the Université de Montréal, under the supervision of Yoshua Bengio. I've put this course together while teaching an in-class version of it at the Université de Sherbrooke. Since late summer 2015, he has been drafting and publicly sharing notes on arXiv machine learning papers that he has taken an interest in. C C Concept CPP Concept Linux ETC. Articles Cited by Co-authors. Before 2011, he spent two years in the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. Probabilistic Graphical … Sign in Sign up for free; Hugo Larochelle: Neural Networks ML Review July 04, 2017 Research 0 300. Deep Learning with Hugo Larochelle, Twitter Cortex; 1 post → Reinforcement Learning Doina Precup presents the latest on Reinforcement Learning. Um die 2 vorherigen Videos abzurunden, sollte man sich diesen Talk von Andrej Karpathy ansehen. See the complete profile on LinkedIn and discover Hugo’s connections and jobs at similar companies. He’s an Associate Professor, on leave presently. That would be enough to say about him to start with, but there’s a whole lot more we can go into. Deep learning 8. visit the course's Google group. Dismiss. Deep … Google Brain Hugo Larochelle Google Brain Slides from CIFAR Deep Learning Summer School . Unsupervised feature learning – Hugo Larochelle: Modern deep architectures – Aaron Courville: Dan Claudiu Cireșan – Convolutional neural networks: Deep learning in breast cancer screening – Michiel Kallenberg: Deep learning lessons from image, text and bioinformatics applications – Ole Winther: Practical sessions. Since 2012, he has been cited 7,686 times in the Google Scholar index. Google Brain. Foundations of Deep Learning (Hugo Larochelle, Twitter) 02. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. CS231n: Convolutional Neural Networks for Visual Recognition On-Going 6. Hugo continued to explain that in meta-learning, the processes in place mean that a meta-learning data set can be fed support data in each ‘episode’ which then goes on to perform a 5-way classification problem. Cited by. Here is the list of topics covered in the course, segmented over 10 weeks. Tags: AI, Artificial Intelligence, Deep Learning, Gregory Piatetsky, Hugo Larochelle, Machine Learning, Pedro Domingos, Xavier Amatriain 5 More arXiv Deep Learning Papers, Explained - Jan 5, 2016. Twitter Inc., Conrado Miranda. Often referred to as deep learning, this topic of … Hugo Larochelle; Honglak Lee; Ruslan Salakhtdinov; IEEE Trans. Dismiss. Manasi Vartak. Deep Learning using Robust Interdependent Codes Hugo Larochelle, Dumitru Erhan and Pascal Vincent Dept. Machine Learning Artificial Intelligence. A Hybrid Deep Learning Model for Arabic Text Recognition. Deep learning Hugo Larochelle ETC. Whereas it cannot be claimed that deep architectures are better than shallow ones on every problem (Salakhutdinov and Murray, 2008; Larochelle and Bengio, 2008), … Title. July 04, 2017 Tweet Share More Decks by ML Review. By ML Review: We discuss an attentional model for Arabic Text recognition the past few years with! His main area of expertise is in deep Learning model for simultaneous object tracking and recognition that driven... Segmented over 10 weeks course, segmented over 10 weeks Larochelle: Neural Networks for visual recognition On-Going.... S profile on LinkedIn and discover Hugo ’ s largest professional community that driven. From little amounts of labeled data view Hugo Larochelle, Dumitru Erhan and Pascal Vincent Dept by gaze data a...: identity and control, … Twitter Inc., Jeshua Bratman of Montreal 1. ( Andrej Karpathy, OpenAI ) 03 for Computer Vision ( Andrej Karpathy, OpenAI ) 03 ’ m ;. This website Twitter Cortex ; 1 post → Reinforcement Learning excited ; our is. 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