A comparison of casting and spiraling algorithms for odor source localization in laminar flow. - Thomas Lochmatter; Xavier Raemy; Loïc Matthey; Saurabh Indra; Alcherio Martinoli (ICRA, 2008)

Experimental study of limit cycle and chaotic controllers for the locomotion of centipede robots. - Loic Matthey; Ludovic Righetti; Auke Jan Ijspeert (IROS, 2008)

Stochastic strategies for a swarm robotic assembly system. - Loic Matthey; Spring Berman; Vijay Kumar 001 (ICRA, 2009)

Aggregation-mediated collective perception and action in a group of miniature robots. - Grégory Mermoud; Loïc Matthey; William C. Evans; Alcherio Martinoli (AAMAS, 2010)

A Probabilistic Palimpsest Model of Visual Short-term Memory. - Loic Matthey; Paul M. Bays; Peter Dayan (PLoS Comput. Biol., 2015)

Early Visual Concept Learning with Unsupervised Deep Learning. - Irina Higgins; Loïc Matthey; Xavier Glorot; Arka Pal; Benigno Uria; Charles Blundell; Shakir Mohamed; Alexander Lerchner (CoRR, 2016)

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. - Irina Higgins; Loïc Matthey; Arka Pal; Christopher P. Burgess; Xavier Glorot; Matthew M. Botvinick; Shakir Mohamed; Alexander Lerchner (ICLR (Poster), 2017)

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. - Irina Higgins; Arka Pal; Andrei A. Rusu; Loïc Matthey; Christopher P. Burgess; Alexander Pritzel; Matthew M. Botvinick; Charles Blundell; Alexander Lerchner (ICML, 2017)

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. - Irina Higgins; Nicolas Sonnerat; Loïc Matthey; Arka Pal; Christopher P. Burgess; Matthew M. Botvinick; Demis Hassabis; Alexander Lerchner (CoRR, 2017)

SCAN: Learning Hierarchical Compositional Visual Concepts. - Irina Higgins; Nicolas Sonnerat; Loic Matthey; Arka Pal; Christopher P. Burgess; Matko Bosnjak; Murray Shanahan; Matthew M. Botvinick; Demis Hassabis; Alexander Lerchner (ICLR (Poster), 2018)

Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. - Alessandro Achille; Tom Eccles; Loïc Matthey; Christopher P. Burgess; Nicholas Watters; Alexander Lerchner; Irina Higgins (NeurIPS, 2018)

Understanding disentangling in β-VAE. - Christopher P. Burgess; Irina Higgins; Arka Pal; Loïc Matthey; Nick Watters; Guillaume Desjardins; Alexander Lerchner (CoRR, 2018)

Towards a Definition of Disentangled Representations. - Irina Higgins; David Amos; David Pfau; Sébastien Racanière; Loïc Matthey; Danilo J. Rezende; Alexander Lerchner (CoRR, 2018)

Multi-Object Representation Learning with Iterative Variational Inference. - Klaus Greff; Raphaël Lopez Kaufman; Rishabh Kabra; Nick Watters; Chris Burgess 001; Daniel Zoran; Loic Matthey; Matthew M. Botvinick; Alexander Lerchner (ICML, 2019)

Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs. - Nicholas Watters; Loïc Matthey; Christopher P. Burgess; Alexander Lerchner (CoRR, 2019)

MONet: Unsupervised Scene Decomposition and Representation. - Christopher P. Burgess; Loïc Matthey; Nicholas Watters; Rishabh Kabra; Irina Higgins; Matthew M. Botvinick; Alexander Lerchner (CoRR, 2019)

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration. - Nicholas Watters; Loic Matthey; Matko Bosnjak; Christopher P. Burgess; Alexander Lerchner (CoRR, 2019)

A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. - Sunny Duan; Nicholas Watters; Loic Matthey; Chris Burgess 001; Alexander Lerchner; Irina Higgins (CoRR, 2019)

Unsupervised Model Selection for Variational Disentangled Representation Learning. - Sunny Duan; Loic Matthey; Andre Saraiva 001; Nick Watters; Chris Burgess 001; Alexander Lerchner; Irina Higgins (ICLR, 2020)

AlignNet: Unsupervised Entity Alignment. - Antonia Creswell; Kyriacos Nikiforou; Oriol Vinyals; Andre Saraiva 001; Rishabh Kabra; Loic Matthey; Chris Burgess 001; Malcolm Reynolds; Richard Tanburn; Marta Garnelo; Murray Shanahan (CoRR, 2020)

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. - Jane Wang 001; 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; et al. (NeurIPS Datasets and Benchmarks, 2021)

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. - Rishabh Kabra; Daniel Zoran; Goker Erdogan; Loic Matthey; Antonia Creswell; Matt M. Botvinick; Alexander Lerchner; Christopher P. Burgess (NeurIPS, 2021)

Alchemy: A structured task distribution for meta-reinforcement learning. - 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; et al. (CoRR, 2021)

Constellation: Learning relational abstractions over objects for compositional imagination. - James C. R. Whittington; Rishabh Kabra; Loic Matthey; Christopher P. Burgess; Alexander Lerchner (CoRR, 2021)

Combining Behaviors with the Successor Features Keyboard. - Wilka Carvalho; Andre Saraiva 001; Angelos Filos; Andrew K. Lampinen; Loic Matthey; Richard L. Lewis; Honglak Lee; Satinder Singh 001; Danilo Jimenez Rezende; Daniel Zoran (NeurIPS, 2023)

Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects. - Rishabh Kabra; Loic Matthey; Alexander Lerchner; Niloy J. Mitra (CoRR, 2023)

SODA: Bottleneck Diffusion Models for Representation Learning. - Drew A. Hudson; Daniel Zoran; Mateusz Malinowski; Andrew K. Lampinen; Andrew Jaegle; James L. McClelland; Loic Matthey; Felix Hill; Alexander Lerchner (CVPR, 2024)

Leveraging VLM-Based Pipelines to Annotate 3D Objects. - Rishabh Kabra; Loic Matthey; Alexander Lerchner; Niloy J. Mitra (ICML, 2024)

Scaling Instructable Agents Across Many Simulated Worlds. - 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; et al. (CoRR, 2024)