Higgins, Irina

MONet: Unsupervised Scene Decomposition and Representation

Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., & Lerchner, A. (2019) · arXiv

Advanced Neural Network ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 270

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

Darla Improving zero-shot transfer in reinforcement learning

Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M., Blundell, C., & Lerchner, A. (2017) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 219

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons

Higgins, I., Chang, L., Langston, V., Hassabis, D., Summerfield, C., Tsao, D., & Botvinick, M. (2021) · Nature Communications

Face Recognition and PerceptionFace recognition and analysis

Cited by 162

Solving math word problems with process- and outcome-based feedback

Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., & Higgins, I. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 25