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ilya sutskever h index

He is the co-inventor, with Alexander Krizhevsky and Geoffrey Hinton, of AlexNet, a convolutional neural network. 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. ImageNet classification with deep convolutional neural networks. The goal of this implementation is to be simple, highly extensible, and easy to integrate into your own projects. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. Profile was last updated at November 28, 2020, 2:53 am Guide2Research Ranking is based on Google Scholar H-Index. Flow++: Improving flow-based generative models with variational dequantization and architecture design. Rotate Clockwise Rotate Counterclockwise. The ones marked. Share templates between classes. You are currently offline. This paper describes the TensorFlow interface for expressing machine learning algorithms, and an implementation of that interface that we have built at Google. 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. OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. Dropping half of the feature detectors from a feedforward neural network reduces overfitting and improves performance on held-out test data. [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. 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} } Jonathan Ho, Evan Lohn, Pieter Abbeel. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. At the moment, you can easily: 1. Ng. Text Selection Tool Hand Tool. Reproduced with permission. Geoffrey Hinton. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 4 - … ‪Co-Founder and Chief Scientist of OpenAI‬ - ‪Cited by 207,537‬ - ‪Machine Learning‬ - ‪Neural Networks‬ - ‪Artificial Intelligence‬ - ‪Deep Learning‬ 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. We present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible. Distributed representations of words and phrases and their composi-tionality. University of Toronto. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Ilya Sutskever is a computer scientist working in machine learning and currently serving as the Chief scientist of OpenAI. Dropout, the most suc-cessful techniquefor regularizingneural networks, … Language Models are Unsupervised Multitask Learners. In recent years, natural language processing (NLP) has become one of the most important areas with various applications in human's life. Ilya Sutskever A thesis - Department of Computer Science ... Thumbnails Document Outline Attachments. Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... GE Hinton, N Srivastava, A Krizhevsky, I Sutskever, RR Salakhutdinov. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. DOI: 10.1145/3065386 Corpus ID: 195908774. Highlight all Match case. He has made several major contributions to the field of deep learning. Try again later. Presentation Mode Open Print Download Current View. Load pretrained AlexNet models 2. Compression with flows via local bits-back coding. 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. Sequence to Sequence Learning with Neural Networks. 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. Publications. 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. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into … 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? Semantic Scholar profile for Ilya Sutskever, with 18338 highly influential citations and 91 scientific research papers. Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, Ilya Sutskever. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 23, Issue 2, March 2010, Pages 239-243. The system can't perform the operation now. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Doctoral advisor. Ilya Sutskever and Geoffrey Hinton, Neural Networks, Vol. Well known AI researcher (and former Google employee) Ilya Sutskever will be the group's research director. The undefined expres- ICML (3) 2013 : 1139-1147 Previous. 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 We find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. Exploiting Similarities among Languages for Machine Translation. 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. Justin Johnson September 28, 2020 AlexNet Lecture 8 - 30 Figure copyright Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, 2012. It paid another leading researcher, Ian Goodfellow, more than $800,000 — … Distributed Representations of Words and Phrases and their Compositionality. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 4 - April 16, 2020 ... Ilya Sutskever, and Geoffrey Hinton, 2012. This implementation is a work in progress -- new features are currently being implemented. 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. Dropout: a simple way to prevent neural networks from overfitting. Neural Information Processing Systems, 2019. The following articles are merged in Scholar. h W1 W2 s 3072 100 10 Learn 100 templates instead of 10. 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. As the most fundamental task, the field of word embedding still requires more attention and research. 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}} This repository contains an op-for-op PyTorch reimplementation of AlexNet. Mastering the game of Go with deep neural networks and tree search. Some features of the site may not work correctly. In Proceedings of the 26th Annual International Conference on Machine Learning , pages 609-616. Please contact us through the Feedback form below to learn about getting access to the Microsoft Academic Graph. Next. Use AlexNet models for classification or feature extraction Upcoming features: In the next fe… Their, This "Cited by" count includes citations to the following articles in Scholar. Related: Elon Musk gives $10M to fight killer robots. 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. H. Lee, R. Grosse, R. Ranganath, and A.Y. Improving neural networks by preventing co-adaptation of feature detectors. 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. Reproduced with permission. Author pages are created from data sourced from our academic publisher partnerships and public sources. This paper develops a method that can automate the process of generating and extending dictionaries and translation tables for any language pairs. You can run your own complex academic analytics using our data.

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December 2nd, 2020

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