ilya sutskever h index

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

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