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
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
dSprites - Disentanglement testing Sprites dataset
L Matthey, I Higgins, D Hassabis, A Lercher, 2017
Scan: Learning hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
Hamiltonian generative networks
P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
Life-long disentangled representation learning with cross-domain latent homologies
A Achille, T Eccles, L Matthey, CP Burgess, N Watters, A Lerchner, ...
arXiv preprint arXiv:1808.06508, 2018
Equivariant hamiltonian flows
DJ Rezende, S Racanière, I Higgins, P Toth
arXiv preprint arXiv:1909.13739, 2019
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
The Multi-Entity Variational Autoencoder
C Nash, A Eslami, CP Burgess, I Higgins, D Zoran, W Theophane, ..., 2017
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons
I Higgins, L Chang, V Langston, D Hassabis, C Summerfield, D Tsao, ...
arXiv preprint arXiv:2006.14304, 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
Disentangling by subspace diffusion
D Pfau, I Higgins, A Botev, S Racanière
arXiv preprint arXiv:2006.12982, 2020
The role of independent motion in object segmentation in the ventral visual stream: Learning to recognise the separate parts of the body
IV Higgins, SM Stringer
Vision research 51 (6), 553-562, 2011
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
Learning view invariant recognition with partially occluded objects
JM Tromans, I Higgins, SM Stringer
Frontiers in computational neuroscience 6, 48, 2012
Harmonic training and the formation of pitch representation in a neural network model of the auditory brain
N Ahmad, I Higgins, KMM Walker, SM Stringer
Frontiers in computational neuroscience 10, 24, 2016
Training variational autoencoders to generate disentangled latent factors
L Matthey-de-l'Endroit, AT Pal, S Mohamed, X Glorot, I Higgins, ...
US Patent 10,373,055, 2019
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