Silver, David

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 43,764

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 30,357

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

Continuous control with deep reinforcement learning

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 10,782

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 9,471

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 9,084

Asynchronous Methods for Deep Reinforcement Learning

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 7,132

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,562

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,378

Deep Reinforcement Learning with Double Q-Learning

Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,318

Prioritized Experience Replay

Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,120

Rainbow: Combining Improvements in Deep Reinforcement Learning

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1,846

Mastering Atari, Go, chess and shogi by planning with a learned model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,214

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2017) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1,102

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 662

Emergence of Locomotion Behaviours in Rich Environments

Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 657

Discovering faster matrix multiplication algorithms with reinforcement learning

Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R. Ruiz, F. J., Schrittwieser, J., Swirszcz, G., et al. (2022) · Nature

Complexity and Algorithms in GraphsArtificial Intelligence Applications

Cited by 478

Reward is enough

Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021) · Artificial Intelligence

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 445

Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 347

Implicit Quantile Networks for Distributional Reinforcement Learning

Dabney, W., Ostrovski, G., Silver, D., & Munos, R. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 331

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306