Teh, Yee Whye

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Lee, J., Lee, Y., Kim, J., Kosiorek, A. R., Choi, S., & Teh, Y. W. (2018) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 691

Ai for social good: unlocking the opportunity for positive impact

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. V. D., Mugisha, F., et al. (2020) · Nature Communications

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 357

A Fast and Simple Algorithm for Training Neural Probabilistic Language Models

Mnih, A., & Teh, Y. W. (2012) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 315

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

Augmented Neural ODEs

Dupont, E., Doucet, A., & Teh, Y. W. (2019) · arXiv

Neural Networks and ApplicationsModel Reduction and Neural Networks

Cited by 285

Stacked capsule autoencoders

Kosiorek, A. R., Sabour, S., Teh, Y. W., & Hinton, G. E. (2019) · arXiv

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 163

Meta reinforcement learning as task inference

Humplik, J., Galashov, A., Hasenclever, L., Ortega, P. A., Teh, Y. W., & Heess, N. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 82

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

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

Bayesian Deep Ensembles via the Neural Tangent Kernel

He, B., Lakshminarayanan, B., & Teh, Y. W. (2020) · arXiv

Gaussian Processes and Bayesian InferenceAdversarial Robustness in Machine Learning

Cited by 36

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning

Miao, N., Teh, Y. W., & Rainforth, T. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 18

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

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models

Rannen-Triki, A., Bornschein, J., Pascanu, R., Hutter, M., György, A., Galashov, A., Teh, Y. W., & Titsias, M. K. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 1