Sutskever, Ilya

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 112,155

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 24,533

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 23,372

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 17,766

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

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature

Artificial Intelligence in GamesArtificial Intelligence Applications

Cited by 15,612

Learning Transferable Visual Models From Natural Language Supervision

Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 15,293

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv

Parallel Computing and Optimization TechniquesCloud Computing and Resource Management

Cited by 9,408

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv

Adversarial Robustness in Machine LearningNeural Networks and Applications

Cited by 8,143

Improving neural networks by preventing co-adaptation of feature detectors

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 6,382

Zero-shot text-to-image generation

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 2,417

Robust Speech Recognition via Large-Scale Weak Supervision

Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2022) · arXiv

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,953

Evaluating Large Language Models Trained on Code

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv

Software Engineering ResearchNatural Language Processing Techniques

Cited by 1,436

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., & Chen, M. (2021) · arXiv

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 1,357

Dota 2 with Large Scale Deep Reinforcement Learning

OpenAI, :, Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., et al. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,065

Generating Long Sequences with Sparse Transformers

Child, R., Gray, S., Radford, A., & Sutskever, I. (2019) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 1,063

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., & Duvenaud, D. (2018) · arXiv

Generative Adversarial Networks and Image SynthesisMarkov Chains and Monte Carlo Methods

Cited by 796

Deep double descent: where bigger models and more data hurt*

Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., & Sutskever, I. (2021) · Journal of Statistical Mechanics: Theory and Experiment

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 565

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 503

Grammar as a Foreign Language

Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 464

Learning to Generate Reviews and Discovering Sentiment

Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 330

GPT-4o System Card

OpenAI, :, Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 180

Consistency Models

Song, Y., Dhariwal, P., Chen, M., & Sutskever, I. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 155

Generative Language Modeling for Automated Theorem Proving

Polu, S., & Sutskever, I. (2020) · arXiv

Logic, programming, and type systemsMathematics, Computing, and Information Processing

Cited by 82

Some Considerations on Learning to Explore via Meta-Reinforcement Learning

Stadie, B. C., Yang, G., Houthooft, R., Chen, X., Duan, Y., Wu, Y., Abbeel, P., & Sutskever, I. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 74