Budden, David

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

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DeepMind Control Suite

Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. D. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

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Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Unified Scaling Laws for Routed Language Models

Clark, A., Casas, D. D. L., Guy, A., Mensch, A., Paganini, M., Hoffmann, J., Damoc, B., Hechtman, B., Cai, T., Borgeaud, S., et al. (2022) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

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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

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Botev, A., De, S., Smith, S. L., Fernando, A., Muraru, G.-C., Haroun, R., Berrada, L., Pascanu, R., Sessa, P. G., Dadashi, R., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5