Irina Higgins
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beta-vae: Learning basic visual concepts with a constrained variational framework
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
Darla: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ...
International Conference on Machine Learning, 1480-1490, 2017
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
dSprites - Disentanglement testing Sprites dataset
L Matthey, I Higgins, D Hassabis, A Lercher, 2017
Scaling language models: Methods, analysis & insights from training gopher
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
Hamiltonian generative networks
P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
Scan: Learning hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
Life-long disentangled representation learning with cross-domain latent homologies
A Achille, T Eccles, L Matthey, C Burgess, N Watters, A Lerchner, ...
Advances in Neural Information Processing Systems 31, 2018
Unsupervised Model Selection for Variational Disentangled Representation Learning
S Duan, L Matthey, A Saraiva, N Watters, CP Burgess, A Lerchner, ...
arXiv preprint arXiv:1905.12614, 2019
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
I Higgins, L Chang, V Langston, D Hassabis, C Summerfield, D Tsao, ...
Nature communications 12 (1), 1-14, 2021
Equivariant hamiltonian flows
DJ Rezende, S Racanière, I Higgins, P Toth
arXiv preprint arXiv:1909.13739, 2019
The Multi-Entity Variational Autoencoder
C Nash, A Eslami, CP Burgess, I Higgins, D Zoran, W Theophane, ..., 2017
Disentangled cumulants help successor representations transfer to new tasks
C Grimm, I Higgins, A Barreto, D Teplyashin, M Wulfmeier, T Hertweck, ...
arXiv preprint arXiv:1911.10866, 2019
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
A Creswell, M Shanahan, I Higgins
arXiv preprint arXiv:2205.09712, 2022
Disentangling by subspace diffusion
D Pfau, I Higgins, A Botev, S Racanière
Advances in Neural Information Processing Systems 33, 17403-17415, 2020
Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain
I Higgins, S Stringer, J Schnupp
PLoS One 12 (8), e0180174, 2017
Which priors matter? Benchmarking models for learning latent dynamics
A Botev, A Jaegle, P Wirnsberger, D Hennes, I Higgins
arXiv preprint arXiv:2111.05458, 2021
Representation matters: improving perception and exploration for robotics
M Wulfmeier, A Byravan, T Hertweck, I Higgins, A Gupta, T Kulkarni, ...
2021 IEEE International Conference on Robotics and Automation (ICRA), 6512-6519, 2021
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