Следене
Irina Jurenka
Irina Jurenka
DeepMind
Потвърден имейл адрес: google.com
Заглавие
Позовавания
Позовавания
Година
beta-vae: Learning basic visual concepts with a constrained variational framework.
I Higgins, L Matthey, A Pal, CP Burgess, X Glorot, MM Botvinick, ...
ICLR (Poster) 3, 2017
50922017
Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
11312018
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
8162021
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
5142019
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
5062018
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
4972017
dSprites - Disentanglement testing Sprites dataset
L Matthey, I Higgins, D Hassabis, A Lercher
https://github.com/deepmind/dsprites-dataset, 2017
4072017
Selection-inference: Exploiting large language models for interpretable logical reasoning
A Creswell, M Shanahan, I Higgins
arXiv preprint arXiv:2205.09712, 2022
2272022
Hamiltonian generative networks
P Toth, DJ Rezende, A Jaegle, S Racanière, A Botev, I Higgins
arXiv preprint arXiv:1909.13789, 2019
2182019
International Conference on Learning Representations
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
ICLR 2017, Toulon, France, 2017
1662017
Scan: Learning hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
1612017
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
1392018
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), 6456, 2021
1272021
Solving math word problems with process-and outcome-based feedback
J Uesato, N Kushman, R Kumar, F Song, N Siegel, L Wang, A Creswell, ...
arXiv preprint arXiv:2211.14275, 2022
872022
Cyprien de Masson d’Autume
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
842021
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
802019
Equivariant hamiltonian flows
DJ Rezende, S Racanière, I Higgins, P Toth
arXiv preprint arXiv:1909.13739, 2019
612019
Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, HF Song, J Aslanides, ...
Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William S. Isaac …, 2021
512021
Understanding disentangling in β
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
512018
Symmetry-based representations for artificial and biological general intelligence
I Higgins, S Racanière, D Rezende
Frontiers in Computational Neuroscience 16, 836498, 2022
422022
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