Maddison, Chris J.

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

Conditional Neural Processes

Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., & Eslami, S. M. A. (2018) · arXiv

Gaussian Processes and Bayesian InferenceNeural Networks and Applications

Cited by 287

Tighter Variational Bounds are Not Necessarily Better

Rainforth, T., Kosiorek, A. R., Le, T. A., Maddison, C. J., Igl, M., Wood, F., & Teh, Y. W. (2018) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 66

Identifying the Risks of LM Agents with an LM-Emulated Sandbox

Ruan, Y., Dong, H., Wang, A., Pitis, S., Zhou, Y., Ba, J., Dubois, Y., Maddison, C. J., & Hashimoto, T. (2023) · arXiv

Adversarial Robustness in Machine LearningSoftware Testing and Debugging Techniques

Cited by 19

Observational Scaling Laws and the Predictability of Language Model Performance

Ruan, Y., Maddison, C. J., & Hashimoto, T. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3