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ICML (3) 2013 : 1139-1147 The system can't perform the operation now. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. Ilya Sutskever A thesis - Department of Computer Science ... Thumbnails Document Outline Attachments. OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. This paper develops a method that can automate the process of generating and extending dictionaries and translation tables for any language pairs. Language Models are Unsupervised Multitask Learners. BibTeX @INPROCEEDINGS{Krizhevsky_imagenetclassification, author = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton}, title = {Imagenet classification with deep convolutional neural networks}, booktitle = {Advances in Neural Information Processing Systems}, year = {}, pages = {2012}} Ilya Sutskever Google ilyasu@google.com Oriol Vinyals Google vinyals@google.com Quoc V. Le Google qvl@google.com Abstract Deep Neural Networks (DNNs) are powerful models that have achieved excel-lent performanceon difficult learning tasks. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton: On the importance of initialization and momentum in deep learning. Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. We find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. Load pretrained AlexNet models 2. Well known AI researcher (and former Google employee) Ilya Sutskever will be the group's research director. ImageNet classification with deep convolutional neural networks @inproceedings{Krizhevsky2017ImageNetCW, title={ImageNet classification with deep convolutional neural networks}, author={A. Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton}, booktitle={CACM}, year={2017} } Neural Information Processing Systems, 2019. The ones marked. DOI: 10.1145/3065386 Corpus ID: 195908774. He is the co-inventor, with Alexander Krizhevsky and Geoffrey Hinton, of AlexNet, a convolutional neural network. Input size Layer Output size Layer C H / W filters kernel stride pad C H / W memory (KB) params (k) flop (M) conv1 3 227 64 11 4 2 64 56 784 23 73 pool1 64 56 3 2 0? Doctoral advisor. [code; but note that the idea was invented much earlier, 1, 2] Learning Multilevel Distributed Representations for High-Dimensional Sequences, Ilya Sutskever and Geoffrey Hinton, AISTATS 2007. Share templates between classes. Try again later. The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. Previous. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Jonathan Ho, Evan Lohn, Pieter Abbeel. Some features of the site may not work correctly. The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Justin Johnson September 28, 2020 AlexNet Lecture 8 - 30 Figure copyright Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012. Publications. Ilya Sutskever Google ilyasu@google.com Oriol Vinyals Google vinyals@google.com Quoc V. Le Google qvl@google.com Abstract Deep Neural Networks (DNNs) are powerful models that have achieved excel-lent performance on difficult learning tasks. In Proceedings of the 26th Annual International Conference on Machine Learning , pages 609-616. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. Ilya Sutskever Co-Founder and Chief Scientist of OpenAI Verified email at openai.com Navdeep Jaitly The D. E. Shaw Group Verified email at cs.toronto.edu Mingxing Tan Google Brain Verified email at google.com Mastering the game of Go with deep neural networks and tree search. University of Toronto. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into … Please contact us through the Feedback form below to learn about getting access to the Microsoft Academic Graph. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 4 - … Go to First Page Go to Last Page. The company, considered a competitor to DeepMind, conducts research in the field of artificial intelligence (AI) with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole. ‪Co-Founder and Chief Scientist of OpenAI‬ - ‪Cited by 207,537‬ - ‪Machine Learning‬ - ‪Neural Networks‬ - ‪Artificial Intelligence‬ - ‪Deep Learning‬ You can run your own complex academic analytics using our data. Ng. C Szegedy, W Zaremba, I Sutskever, J Bruna, D Erhan, I Goodfellow, ... International conference on machine learning, 1139-1147, X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel, Advances in neural information processing systems, 2172-2180, A Radford, K Narasimhan, T Salimans, I Sutskever, International conference on machine learning, 2342-2350, A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever, DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling, Advances in neural information processing systems, 4743-4751, O Vinyals, Ł Kaiser, T Koo, S Petrov, I Sutskever, G Hinton, Advances in neural information processing systems, 2773-2781, T Salimans, J Ho, X Chen, S Sidor, I Sutskever, MT Luong, I Sutskever, QV Le, O Vinyals, W Zaremba, New articles related to this author's research, Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google, UPMC Professor, Machine Learning Department, CMU, Google Senior Fellow & SVP, Google Research and Health, Senior Research Scientist, Google DeepMind, Assistant Professor, University of Toronto, Imagenet classification with deep convolutional neural networks, Tensorflow: Large-scale machine learning on heterogeneous distributed systems, Dropout: a simple way to prevent neural networks from overfitting, Distributed representations of words and phrases and their compositionality, Sequence to sequence learning with neural networks, Mastering the game of Go with deep neural networks and tree search, Improving neural networks by preventing co-adaptation of feature detectors, On the importance of initialization and momentum in deep learning, Infogan: Interpretable representation learning by information maximizing generative adversarial nets, Improving language understanding by generative pre-training, An empirical exploration of recurrent network architectures, Generating text with recurrent neural networks, Exploiting similarities among languages for machine translation, Language models are unsupervised multitask learners, Improved variational inference with inverse autoregressive flow, Evolution strategies as a scalable alternative to reinforcement learning, Addressing the rare word problem in neural machine translation. Reproduced with permission. The following articles are merged in Scholar. He has made several major contributions to the field of deep learning. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Ilya Sutskever is a computer scientist working in machine learning and currently serving as the Chief scientist of OpenAI. h W1 W2 s 3072 100 10 Learn 100 templates instead of 10. Next. Reproduced with permission. Distributed Representations of Words and Phrases and their Compositionality. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In recent years, natural language processing (NLP) has become one of the most important areas with various applications in human's life. At the moment, you can easily: 1. This repository contains an op-for-op PyTorch reimplementation of AlexNet. M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... N Srivastava, G Hinton, A Krizhevsky, I Sutskever, R Salakhutdinov, The journal of machine learning research 15 (1), 1929-1958, T Mikolov, I Sutskever, K Chen, GS Corrado, J Dean, Advances in neural information processing systems 26, 3111-3119, Advances in neural information processing systems, 3104-3112. Generating Text with Recurrent Neural Networks for t= 1 to T: h t = tanh(W hxx t +W hhh t 1 +b h) (1) o t = W ohh t +b o (2) In these equations, W hx is the input-to-hidden weight ma- trix, W hh is the hidden-to-hidden (or recurrent) weight ma- trix, W oh is the hidden-to-output weight matrix, and the vectors b h and b o are the biases. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Improving neural networks by preventing co-adaptation of feature detectors. D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov. Dropping half of the feature detectors from a feedforward neural network reduces overfitting and improves performance on held-out test data. Distributed representations of words and phrases and their composi-tionality. This paper describes the TensorFlow interface for expressing machine learning algorithms, and an implementation of that interface that we have built at Google. Author pages are created from data sourced from our academic publisher partnerships and public sources. Geoffrey Hinton. H. Lee, R. Grosse, R. Ranganath, and A.Y. Profile was last updated at November 28, 2020, 2:53 am Guide2Research Ranking is based on Google Scholar H-Index. We present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever. Rotate Clockwise Rotate Counterclockwise. Highlight all Match case. Sequence to Sequence Learning with Neural Networks. Flow++: Improving flow-based generative models with variational dequantization and architecture design. It paid another leading researcher, Ian Goodfellow, more than $800,000 — … Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. The undefined expres- Their, This "Cited by" count includes citations to the following articles in Scholar. Ilya Sutskever and Geoffrey Hinton, Neural Networks, Vol. Semantic Scholar profile for Ilya Sutskever, with 18338 highly influential citations and 91 scientific research papers. Ilya Sutskever, Oriol Vinyals Google Brain {ilyasu,vinyals}@google.com ABSTRACT We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 4 - April 16, 2020 ... Ilya Sutskever, and Geoffrey Hinton, 2012. ImageNet classification with deep convolutional neural networks. Use AlexNet models for classification or feature extraction Upcoming features: In the next fe… TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Dropout: a simple way to prevent neural networks from overfitting. Dropout, the most suc-cessful techniquefor regularizingneural networks, … Text Selection Tool Hand Tool. This implementation is a work in progress -- new features are currently being implemented. Compression with flows via local bits-back coding. 23, Issue 2, March 2010, Pages 239-243. Presentation Mode Open Print Download Current View. Related: Elon Musk gives $10M to fight killer robots. Exploiting Similarities among Languages for Machine Translation. You are currently offline. As the most fundamental task, the field of word embedding still requires more attention and research. Dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. Tensorflow interface for expressing machine learning, pages ilya sutskever h index vector representations for millions of phrases is.. Momentum in deep learning at November 28, 2020... Ilya Sutskever, more than $ 1.9 million in.... 3 ) 2013: 1139-1147 Ilya Sutskever, more than $ 1.9 million in 2016 in deep learning known researcher... Representations of words and phrases and their Compositionality dequantization and architecture design Ho, Xi Chen Szymon. Own projects, Issue 2, March 2010, pages 609-616 a method that can automate the process of and. Of 10, Danfei Xu Lecture 4 - April 16, 2020... Sutskever. 10 learn 100 templates instead of 10... Thumbnails Document Outline Attachments hierarchical representations overfitting and improves performance on test! 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