Higgins, Irina

10 publications

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

ICLR 2021 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

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

NeurIPS 2020 Disentangling by Subspace Diffusion David Pfau, Irina Higgins, Alex Botev, Sébastien Racanière

ICLR 2020 Hamiltonian Generative Networks Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins

ICLR 2020 Unsupervised Model Selection for Variational Disentangled Representation Learning Sunny Duan, Loic Matthey, Andre Saraiva, Nicholas Watters, Christopher P. Burgess, Alexander Lerchner, Irina Higgins

NeurIPS 2018 Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies Alessandro Achille, Tom Eccles, Loic Matthey, Chris Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins

ICLR 2018 SCAN: Learning Hierarchical Compositional Visual Concepts Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bošnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner

ICLR 2017 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

ICML 2017 DARLA: Improving Zero-Shot Transfer in Reinforcement Learning Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner