Learning view invariant recognition with partially occluded objects. - James M. Tromans; Irina Higgins; Simon M. Stringer (Frontiers Comput. Neurosci., 2012)

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)

Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain. - Nasir Ahmad; Irina Higgins; Kerry M. M. Walker; Simon M. Stringer (Frontiers Comput. Neurosci., 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)

A Computational Account of the Role of Cochlear Nucleus and Inferior Colliculus in Stabilizing Auditory Nerve Firing for Auditory Category Learning. - Irina Higgins; Simon M. Stringer; Jan W. H. Schnupp (Neural Comput., 2018)

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

Equivariant Hamiltonian Flows. - Danilo Jimenez Rezende; Sébastien Racanière; Irina Higgins; Peter Toth (CoRR, 2019)

Disentangled Cumulants Help Successor Representations Transfer to New Tasks. - Christopher Grimm; Irina Higgins; André Barreto 001; Denis Teplyashin; Markus Wulfmeier; Tim Hertweck; Raia Hadsell; Satinder Singh 001 (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)

Hamiltonian Generative Networks. - Peter Toth; Danilo J. Rezende; Andrew Jaegle; Sébastien Racanière; Aleksandar Botev; Irina Higgins (ICLR, 2020)

Disentangling by Subspace Diffusion. - David Pfau; Irina Higgins; Aleksandar Botev; Sébastien Racanière (NeurIPS, 2020)

Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. - Garrett Honke; Irina Higgins; Nina Thigpen; Vladimir Miskovic; Katie Link; Pramod Gupta; Julia Klawohn; Greg Hajcak (CoRR, 2020)

Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. - Garrett Honke; Irina Higgins; Nina Thigpen; Vladimir Miskovic; Katie Link; Sunny Duan; Pramod Gupta; Julia Klawohn; Greg Hajcak (ICLR, 2021)

Representation Matters: Improving Perception and Exploration for Robotics. - Markus Wulfmeier; Arunkumar Byravan; Tim Hertweck; Irina Higgins; Ankush Gupta; Tejas Kulkarni; Malcolm Reynolds; Denis Teplyashin; Roland Hafner; Thomas Lampe; Martin A. Riedmiller (ICRA, 2021)

Which priors matter? Benchmarking models for learning latent dynamics. - Aleksandar Botev; Andrew Jaegle; Peter Wirnsberger; Daniel Hennes; Irina Higgins (NeurIPS Datasets and Benchmarks, 2021)

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. - Irina Higgins; Peter Wirnsberger; Andrew Jaegle; Aleksandar Botev (NeurIPS, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

Generalizing universal function approximators. - Irina Higgins (Nat. Mach. Intell., 2021)

Solving math word problems with process- and outcome-based feedback. - Jonathan Uesato; Nate Kushman; Ramana Kumar; H. Francis Song; Noah Y. Siegel; Lisa Wang; Antonia Creswell; Geoffrey Irving; Irina Higgins (CoRR, 2022)

Symmetry-Based Representations for Artificial and Biological General Intelligence. - Irina Higgins; Sébastien Racanière; Danilo J. Rezende (Frontiers Comput. Neurosci., 2022)

Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. - Antonia Creswell; Murray Shanahan; Irina Higgins (ICLR, 2023)