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)

Minimally Redundant Laplacian Eigenmaps. - David Pfau; Christopher P. Burgess (ICLR (Workshop), 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)

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)

Unsupervised Object-Based Transition Models For 3D Partially Observable Environments. - Antonia Creswell; Rishabh Kabra; Christopher P. Burgess; Murray Shanahan (NeurIPS, 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)

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

Linking vision and motion for self-supervised object-centric perception. - Kaylene C. Stocking; Zak Murez; Vijay Badrinarayanan; Jamie Shotton; Alex Kendall; Claire J. Tomlin; Christopher P. Burgess (CoRR, 2023)