List of works - Ilya Sutskever

An Online Sequence-to-Sequence Model Using Partial Conditioning ⬇️

scientific article published in January 2016

Cardinality Restricted Boltzmann Machines ⬇️

scientific article published in January 2012

Deep, narrow sigmoid belief networks are universal approximators

scientific article

Distributed Representations of Words and Phrases and their Compositionality ⬇️

computer science journal article

Dropout: A Simple Way to Prevent Neural Networks from Overfitting ⬇️

article by Nitish Srivastava et al published 2014 in Journal of Machine Learning Research

Evaluating Large Language Models Trained on Code ⬇️

scientific article

Evolution Strategies as a Scalable Alternative to Reinforcement Learning ⬇️

scientific article (publication date: 10 March 2017)

Generative Pretraining from Pixels ⬇️

scholarly article

Grammar as a Foreign Language ⬇️

ImageNet classification with deep convolutional neural networks

scientific article publised in Communications of the ACM in 2017

Imagenet classification with deep convolutional neural networks ⬇️

scholarly article from the NIPS conference

Improved Variational Inference with Inverse Autoregressive Flow ⬇️

scientific article published in January 2016

Improving Language Understanding by Generative Pre-Training ⬇️

article

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets ⬇️

scientific article published in January 2016

Intriguing properties of neural networks ⬇️

scientific article published on 19 February 2014

Language Models are Few-Shot Learners ⬇️

article

Language Models are Unsupervised Multitask Learners ⬇️

article

Learning Transferable Visual Models From Natural Language Supervision ⬇️

scientific article published on 26 February 2021

Learning to Execute ⬇️

scientific article published on 19 February 2015

Learning to Generate Reviews and Discovering Sentiment ⬇️

scientific article (publication date: 5 April 2017)

Mastering the game of Go with deep neural networks and tree search

scientific article (publication date: 27 January 2016)

Modelling Relational Data using Bayesian Clustered Tensor Factorization ⬇️

scientific article published in January 2009

Neural Programmer: Inducing Latent Programs with Gradient Descent ⬇️

One-Shot Imitation Learning ⬇️

scientific article published in January 2017

Sequence to Sequence Learning with Neural Networks ⬇️

scientific article

Temporal-kernel recurrent neural networks

scientific article published on 5 November 2009

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems ⬇️

scientific article (publication date: 16 March 2016)

The Importance of Sampling in Meta-Reinforcement Learning ⬇️

scholarly article by Bradly Stadie et al published 2018 in Advances in Neural Information Processing Systems 31

List of works by Ilya Sutskever

An Online Sequence-to-Sequence Model Using Partial Conditioning ⬇️

Cardinality Restricted Boltzmann Machines ⬇️

Deep, narrow sigmoid belief networks are universal approximators

Distributed Representations of Words and Phrases and their Compositionality ⬇️

Dropout: A Simple Way to Prevent Neural Networks from Overfitting ⬇️

Evaluating Large Language Models Trained on Code ⬇️

Evolution Strategies as a Scalable Alternative to Reinforcement Learning ⬇️

Generative Pretraining from Pixels ⬇️

Grammar as a Foreign Language ⬇️

ImageNet classification with deep convolutional neural networks

Imagenet classification with deep convolutional neural networks ⬇️

Improved Variational Inference with Inverse Autoregressive Flow ⬇️

Improving Language Understanding by Generative Pre-Training ⬇️

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets ⬇️

Intriguing properties of neural networks ⬇️

Language Models are Few-Shot Learners ⬇️

Language Models are Unsupervised Multitask Learners ⬇️

Learning Transferable Visual Models From Natural Language Supervision ⬇️

Learning to Execute ⬇️

Learning to Generate Reviews and Discovering Sentiment ⬇️

Mastering the game of Go with deep neural networks and tree search

Modelling Relational Data using Bayesian Clustered Tensor Factorization ⬇️

Neural Programmer: Inducing Latent Programs with Gradient Descent ⬇️

One-Shot Imitation Learning ⬇️

Sequence to Sequence Learning with Neural Networks ⬇️

Temporal-kernel recurrent neural networks

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems ⬇️

The Importance of Sampling in Meta-Reinforcement Learning ⬇️

The Recurrent Temporal Restricted Boltzmann Machine ⬇️

Using matrices to model symbolic relationship ⬇️

Zero-Shot Text-to-Image Generation ⬇️