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Journal Articles
- 2015
[j1]Loic Matthey, Paul M. Bays
, Peter Dayan:
A Probabilistic Palimpsest Model of Visual Short-term Memory. PLoS Comput. Biol. 11(1) (2015)
Conference and Workshop Papers
- 2024
- [c15]Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner:
SODA: Bottleneck Diffusion Models for Representation Learning. CVPR 2024: 23115-23127 - [c14]Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra:
Leveraging VLM-Based Pipelines to Annotate 3D Objects. ICML 2024 - 2023
- [c13]Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew K. Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo Jimenez Rezende, Daniel Zoran:
Combining Behaviors with the Successor Features Keyboard. NeurIPS 2023 - 2021
- [c12]Jane Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charles Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matt M. Botvinick:
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. NeurIPS Datasets and Benchmarks 2021 - [c11]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. NeurIPS 2021: 20146-20159 - 2020
- [c10]Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins:
Unsupervised Model Selection for Variational Disentangled Representation Learning. ICLR 2020 - 2019
- [c9]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. ICML 2019: 2424-2433 - 2018
- [c8]Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) 2018 - [c7]Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins:
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. NeurIPS 2018: 9895-9905 - 2017
- [c6]Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner:
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) 2017 - [c5]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML 2017: 1480-1490 - 2010
- [c4]Grégory Mermoud, Loïc Matthey, William C. Evans, Alcherio Martinoli:
Aggregation-mediated collective perception and action in a group of miniature robots. AAMAS 2010: 599-606 - 2009
- [c3]Loic Matthey, Spring Berman, Vijay Kumar:
Stochastic strategies for a swarm robotic assembly system. ICRA 2009: 1953-1958 - 2008
- [c2]Thomas Lochmatter, Xavier Raemy, Loïc Matthey, Saurabh Indra, Alcherio Martinoli:
A comparison of casting and spiraling algorithms for odor source localization in laminar flow. ICRA 2008: 1138-1143 - [c1]Loic Matthey, Ludovic Righetti, Auke Jan Ijspeert:
Experimental study of limit cycle and chaotic controllers for the locomotion of centipede robots. IROS 2008: 1860-1865
Informal and Other Publications
- 2024
- [i19]SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young:
Scaling Instructable Agents Across Many Simulated Worlds. CoRR abs/2404.10179 (2024) - 2023
- [i18]Wilka Carvalho, Andre Saraiva, Angelos Filos, Andrew Kyle Lampinen, Loic Matthey, Richard L. Lewis, Honglak Lee, Satinder Singh, Danilo J. Rezende, Daniel Zoran:
Combining Behaviors with the Successor Features Keyboard. CoRR abs/2310.15940 (2023) - [i17]Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra:
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects. CoRR abs/2311.17851 (2023) - [i16]Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner:
SODA: Bottleneck Diffusion Models for Representation Learning. CoRR abs/2311.17901 (2023) - 2021
- [i15]Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew M. Botvinick:
Alchemy: A structured task distribution for meta-reinforcement learning. CoRR abs/2102.02926 (2021) - [i14]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR abs/2106.03849 (2021) - [i13]James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner:
Constellation: Learning relational abstractions over objects for compositional imagination. CoRR abs/2107.11153 (2021) - 2020
- [i12]Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals, Andre Saraiva, Rishabh Kabra, Loic Matthey, Chris Burgess, Malcolm Reynolds, Richard Tanburn, Marta Garnelo, Murray Shanahan:
AlignNet: Unsupervised Entity Alignment. CoRR abs/2007.08973 (2020) - 2019
- [i11]Nicholas Watters, Loïc Matthey, Christopher P. Burgess, Alexander Lerchner:
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs. CoRR abs/1901.07017 (2019) - [i10]Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner:
MONet: Unsupervised Scene Decomposition and Representation. CoRR abs/1901.11390 (2019) - [i9]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loïc Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. CoRR abs/1903.00450 (2019) - [i8]Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, Alexander Lerchner:
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration. CoRR abs/1905.09275 (2019) - [i7]Sunny Duan, Nicholas Watters, Loic Matthey, Chris Burgess, Alexander Lerchner, Irina Higgins:
A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. CoRR abs/1905.12614 (2019) - 2018
- [i6]Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner:
Understanding disentangling in β-VAE. CoRR abs/1804.03599 (2018) - [i5]Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner, Irina Higgins:
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. CoRR abs/1808.06508 (2018) - [i4]Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner:
Towards a Definition of Disentangled Representations. CoRR abs/1812.02230 (2018) - 2017
- [i3]Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR abs/1707.03389 (2017) - [i2]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR abs/1707.08475 (2017) - 2016
- [i1]Irina Higgins, Loïc Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner:
Early Visual Concept Learning with Unsupervised Deep Learning. CoRR abs/1606.05579 (2016)
Coauthor Index
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