Loic Matthey
Loic Matthey
Adresă de e-mail confirmată pe google.com - Pagina de pornire
Citat de
Citat de
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, ...
International conference on learning representations, 2017
Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
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
DARLA: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Multi-Object Representation Learning with Iterative Variational Inference
K Greff, RL Kaufmann, R Kabra, N Watters, CP Burgess, Z Daniel, ...
arXiv preprint arXiv:1903.00450, 2019
dSprites: Disentanglement testing Sprites dataset
L Matthey, I Higgins, D Hassabis, A Lerchner
https://github.com/deepmind/dsprites-dataset, 2017
Early visual concept learning with unsupervised deep learning
I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
International Conference on Learning Representations
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
ICLR 2017, Toulon, France, 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
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
N Watters, L Matthey, CP Burgess, A Lerchner
arXiv preprint arXiv:1901.07017, 2019
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
Cobra: Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration
N Watters, L Matthey, M Bosnjak, CP Burgess, A Lerchner
arXiv preprint arXiv:1905.09275, 2019
A comparison of casting and spiraling algorithms for odor source localization in laminar flow
T Lochmatter, X Raemy, L Matthey, S Indra, A Martinoli
2008 IEEE International Conference on Robotics and Automation, 1138-1143, 2008
Stochastic strategies for a swarm robotic assembly system
L Matthey, S Berman, V Kumar
2009 IEEE International Conference on Robotics and Automation, 1953-1958, 2009
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
Multi-object datasets
R Kabra, C Burgess, L Matthey, RL Kaufman, K Greff, M Reynolds, ...
DeepMind 5 (6), 7, 2019
Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition
R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ...
Advances in Neural Information Processing Systems 34, 20146-20159, 2021
A probabilistic palimpsest model of visual short-term memory
L Matthey, PM Bays, P Dayan
PLoS computational biology 11 (1), e1004003, 2015
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
JX Wang, M King, N Porcel, Z Kurth-Nelson, T Zhu, C Deck, P Choy, ...
arXiv preprint arXiv:2102.02926, 2021
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