de Freitas, Nando

Dueling Network Architectures for Deep Reinforcement Learning

Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,300

Learning to learn by gradient descent by gradient descent

Andrychowicz, M., Denil, M., Gomez, S., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., & de Freitas, N. (2016) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 1,486

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Foerster, J. N., Assael, Y. M., de Freitas, N., & Whiteson, S. (2016) · arXiv

Reinforcement Learning in RoboticsMulti-Agent Systems and Negotiation

Cited by 1,217

Competition-Level Code Generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al. (2022) · arXiv

Software Engineering ResearchTeaching and Learning Programming

Cited by 1,043

Predicting Parameters in Deep Learning

Denil, M., Shakibi, B., Dinh, L., Ranzato, M., & de Freitas, N. (2013) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 853

Neural Programmer-Interpreters

Reed, S., & de Freitas, N. (2015) · arXiv

Neural Networks and ApplicationsTeaching and Learning Programming

Cited by 347

Critic regularized regression

Wang, Z., Novikov, A., Zolna, K., Springenberg, J. T., Reed, S., Shahriari, B., Siegel, N., Merel, J., Gulcehre, C., Heess, N., et al. (2020) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 128

A Generalist Agent

Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al. (2022) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 63

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

Learning Compositional Neural Programs with Recursive Tree Search and Planning

Pierrot, T., Ligner, G., Reed, S., Sigaud, O., Perrin, N., Laterre, A., Kas, D., Beguir, K., & de Freitas, N. (2019) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 20

Reinforced Self-Training (ReST) for Language Modeling

Gulcehre, C., Paine, T. L., Srinivasan, S., Konyushkova, K., Weerts, L., Sharma, A., Siddhant, A., Ahern, A., Wang, M., Gu, C., et al. (2023) · arXiv

Natural Language Processing TechniquesReinforcement Learning in Robotics

Cited by 20

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

De, S., Smith, S. L., Fernando, A., Botev, A., Cristian-Muraru, G., Gu, A., Haroun, R., Berrada, L., Chen, Y., Srinivasan, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8