Ilya

Ilya Sutskever

#5,299
Most Influential Person Now

AI researcher

Ilya Sutskever's Academic­Influence.com Rankings

Ilya Sutskever
Computer Science
#298
World Rank
#310
Historical Rank
Machine Learning
#29
World Rank
#29
Historical Rank
Algorithms
#30
World Rank
#30
Historical Rank
Computational Linguistics
#42
World Rank
#42
Historical Rank
computer-science Degrees
Download Badge
  • Computer Science

Why Is Ilya Sutskever Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Ilya Sutskever is a computer scientist working in machine learning. He is a co-founder and Chief Scientist at OpenAI. He has made several major contributions to the field of deep learning. In 2023, Sutskever and the OpenAI board fired CEO Sam Altman, who returned a week later. He is the co-inventor, with Alex Krizhevsky and Geoffrey Hinton, of AlexNet, a convolutional neural network. Sutskever is also one of the many co-authors of the AlphaGo paper.

(See a Problem?)

Ilya Sutskever's Published Works

Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
194019501960197019801990200020102020012500250003750050000625007500087500100000112500

Published Works

  • ImageNet classification with deep convolutional neural networks (2012) (96741)
  • Dropout: a simple way to prevent neural networks from overfitting (2014) (32886)
  • Distributed Representations of Words and Phrases and their Compositionality (2013) (29321)
  • Sequence to Sequence Learning with Neural Networks (2014) (17010)
  • Mastering the game of Go with deep neural networks and tree search (2016) (13400)
  • Intriguing properties of neural networks (2013) (10943)
  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) (9876)
  • Language Models are Few-Shot Learners (2020) (9748)
  • Language Models are Unsupervised Multitask Learners (2019) (9533)
  • Improving neural networks by preventing co-adaptation of feature detectors (2012) (6887)
  • Learning Transferable Visual Models From Natural Language Supervision (2021) (4645)
  • On the importance of initialization and momentum in deep learning (2013) (4081)
  • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016) (3593)
  • Recurrent Neural Network Regularization (2014) (2278)
  • An Empirical Exploration of Recurrent Network Architectures (2015) (1541)
  • Exploiting Similarities among Languages for Machine Translation (2013) (1460)
  • Zero-Shot Text-to-Image Generation (2021) (1382)
  • Generating Text with Recurrent Neural Networks (2011) (1375)
  • Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017) (1189)
  • Dota 2 with Large Scale Deep Reinforcement Learning (2019) (1020)
  • Generating Long Sequences with Sparse Transformers (2019) (939)
  • Grammar as a Foreign Language (2014) (884)
  • Continuous Deep Q-Learning with Model-based Acceleration (2016) (862)
  • Generative Pretraining From Pixels (2020) (827)
  • Evaluating Large Language Models Trained on Code (2021) (767)
  • RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning (2016) (761)
  • Multi-task Sequence to Sequence Learning (2015) (742)
  • Addressing the Rare Word Problem in Neural Machine Translation (2014) (717)
  • GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models (2021) (639)
  • Learning Recurrent Neural Networks with Hessian-Free Optimization (2011) (617)
  • Variational Lossy Autoencoder (2016) (576)
  • FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models (2018) (571)
  • Deep double descent: where bigger models and more data hurt (2019) (548)
  • One-Shot Imitation Learning (2017) (522)
  • Learning to Execute (2014) (506)
  • Adding Gradient Noise Improves Learning for Very Deep Networks (2015) (443)
  • Learning to Generate Reviews and Discovering Sentiment (2017) (416)
  • The Recurrent Temporal Restricted Boltzmann Machine (2008) (415)
  • Neural GPUs Learn Algorithms (2015) (323)
  • Jukebox: A Generative Model for Music (2020) (302)
  • Emergent Complexity via Multi-Agent Competition (2017) (295)
  • Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (2017) (290)
  • Training Recurrent Neural Networks (2013) (281)
  • Modelling Relational Data using Bayesian Clustered Tensor Factorization (2009) (271)
  • Neural Programmer: Inducing Latent Programs with Gradient Descent (2015) (258)
  • Learning Multilevel Distributed Representations for High-Dimensional Sequences (2007) (247)
  • Training Deep and Recurrent Networks with Hessian-Free Optimization (2012) (219)
  • Learning Factored Representations in a Deep Mixture of Experts (2013) (194)
  • SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS (2011) (193)
  • Third-Person Imitation Learning (2017) (191)
  • Deep Neural Networks (2013) (178)
  • Reinforcement Learning Neural Turing Machines (2015) (170)
  • Reinforcement Learning Neural Turing Machines - Revised (2015) (155)
  • Deep, Narrow Sigmoid Belief Networks Are Universal Approximators (2008) (149)
  • Neural Random Access Machines (2015) (140)
  • Move Evaluation in Go Using Deep Convolutional Neural Networks (2014) (135)
  • MuProp: Unbiased Backpropagation for Stochastic Neural Networks (2015) (133)
  • Robust Speech Recognition via Large-Scale Weak Supervision (2022) (111)
  • Generative Language Modeling for Automated Theorem Proving (2020) (111)
  • An Online Sequence-to-Sequence Model Using Partial Conditioning (2015) (103)
  • Visualizing Similarity Data with a Mixture of Maps (2007) (102)
  • On the Convergence Properties of Contrastive Divergence (2010) (101)
  • Some Considerations on Learning to Explore via Meta-Reinforcement Learning (2018) (90)
  • GamePad: A Learning Environment for Theorem Proving (2018) (79)
  • Estimating the Hessian by Back-propagating Curvature (2012) (59)
  • A Neural Transducer (2015) (46)
  • Towards Principled Unsupervised Learning (2015) (46)
  • Temporal-Kernel Recurrent Neural Networks (2010) (46)
  • Learning online alignments with continuous rewards policy gradient (2016) (43)
  • Cardinality Restricted Boltzmann Machines (2012) (43)
  • Distribution Augmentation for Generative Modeling (2020) (41)
  • Mimicking Go Experts with Convolutional Neural Networks (2008) (36)
  • Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning (2013) (33)
  • Improving Variational Autoencoders with Inverse Autoregressive Flow (2016) (31)
  • Data Normalization in the Learning of Restricted Boltzmann Machines (2011) (25)
  • Using matrices to model symbolic relationship (2008) (24)
  • Formal Mathematics Statement Curriculum Learning (2022) (23)
  • The Importance of Sampling inMeta-Reinforcement Learning (2018) (22)
  • Parallelizable Sampling of Markov Random Fields (2010) (21)
  • Extensions and Limitations of the Neural GPU (2016) (19)
  • Unsupervised Neural Machine Translation with Generative Language Models Only (2021) (14)
  • A simpler unified analysis of budget perceptrons (2009) (11)
  • An online sequence-to-sequence model for noisy speech recognition (2017) (6)
  • DLVM : A MODERN COMPILER INFRASTRUCTURE FOR DEEP LEARNING (2017) (4)
  • Number 20 (1998) (3)
  • UTML TR 2011 – 002 Data Normalization in the Learning of Restricted Boltzmann Machines (2011) (3)
  • A Closer Look at Gradient Estimators with Reinforcement Learning as Inference (2021) (2)
  • Using matrices to model symbolic relationships (2008) (1)
  • Learning a System-ID Embedding Space for Domain Specialization with Deep Reinforcement Learning (2018) (1)
  • Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model (2021) (1)
  • GAMEPAD: A LEARNING ENVIRONMENT FOR THEO- (2018) (0)
  • Feature-Matching Auto-Encoders (2017) (0)
  • Conditional Augmentation for Generative Modeling (2020) (0)
  • HIRD-P ERSON I MITATION L EARNING (2017) (0)
  • 32 15 v 3 [ cs . C L ] 1 4 D ec 2 01 4 Sequence to Sequence Learning with Neural Networks (2018) (0)
  • MAQA: A Multimodal QA Benchmark for Negation Anonymous Author(s) (2022) (0)
  • CSC 2535 2011 ASSIGNMENT 2 (2011) (0)
  • Generative Models for Alignment and Data Efficiency in Language (2018) (0)
  • ADAPT: Vision-Language Navigation with Modality-Aligned Action Prompts (2022) (0)
  • TensorFlow Distributions Joshua (2017) (0)
  • CSC 2414-Metric Embeddings ∗ Lecture 5 : Dimension Reduction (2006) (0)
  • Consistency Models (2023) (0)

This paper list is powered by the following services:

Metadata from Crossref logo

Other Resources About Ilya Sutskever

What Schools Are Affiliated With Ilya Sutskever?

Ilya Sutskever is affiliated with the following schools: