Mean Field Methods for Cortical Network Dynamics. - John Hertz; Alexander Lerchner; Mandana Ahmadi (Summer School on Neural Networks, 2003)

High-conductance states in a mean-field cortical network model. - Alexander Lerchner; Mandana Ahmadi; John Hertz (Neurocomputing, 2004)

Synaptic model for spontaneous activity in developing networks. - Alexander Lerchner; John Rinzel (Neurocomputing, 2005)

Response Variability in Balanced Cortical Networks. - Alexander Lerchner; Cristina Ursta; John Hertz; Mandana Ahmadi; Pauline Ruffiot; Søren Enemark (Neural Comput., 2006)

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)

PARTS: Unsupervised segmentation with slots, attention and independence maximization. - Daniel Zoran; Rishabh Kabra; Alexander Lerchner; Danilo J. Rezende (ICCV, 2021)

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)

Formalising Concepts as Grounded Abstractions. - Stephen Clark; Alexander Lerchner; Tamara von Glehn; Olivier Tieleman; Richard Tanburn; Misha Dashevskiy; Matko Bosnjak (CoRR, 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)

Reasoning-Modulated Representations. - Petar Velickovic; Matko Bosnjak; Thomas Kipf; Alexander Lerchner; Raia Hadsell; Razvan Pascanu; Charles Blundell (LoG, 2022)

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)

Can foundation models actively gather information in interactive environments to test hypotheses? - Nan Rosemary Ke; Danny P. Sawyer; Hubert Soyer; Martin Engelcke; David P. Reichert; Drew A. Hudson; John Reid; Alexander Lerchner; Danilo Jimenez Rezende; Timothy P. Lillicrap; Michael Mozer; Jane X. Wang (CoRR, 2024)