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, ...
1734 2016 Understanding disentangling in CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
366 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
223 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
134 2018 dSprites - Disentanglement testing Sprites dataset L Matthey, I Higgins, D Hassabis, A Lercher
https://github.com/deepmind/dsprites-dataset, 2017
133 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
118 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
92 * 2017 Hamiltonian generative networks P Toth, DJ Rezende, A Jaegle, S Racaničre, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
62 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
53 2018 Equivariant hamiltonian flows DJ Rezende, S Racaničre, I Higgins, P Toth
arXiv preprint arXiv:1909.13739, 2019
18 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
17 * 2019 The Multi-Entity Variational Autoencoder C Nash, A Eslami, CP Burgess, I Higgins, D Zoran, W Theophane, ...
http://charlienash.github.io/assets/docs/mevae2017.pdf, 2017
15 2017 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
10 2017 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
9 2011 Learning view invariant recognition with partially occluded objects JM Tromans, I Higgins, SM Stringer
Frontiers in computational neuroscience 6, 48, 2012
7 2012 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
6 2019 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
6 2016 Disentangling by Subspace Diffusion D Pfau, I Higgins, A Botev, S Racaničre
arXiv preprint arXiv:2006.12982, 2020
5 2020 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
4 2020 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
2 2019 
Loic MattheyDeepMindEmail verificata su google.com
