Irina Higgins

According to our database, Irina Higgins authored at least 28 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

Antonia Creswell, Murray Shanahan, Irina Higgins

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Symmetry-Based Representations for Artificial and Biological General Intelligence.

Irina Higgins, Sébastien Racanière, Danilo J. Rezende

Frontiers Comput. Neurosci., 2022

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

2021

Generalizing universal function approximators.

Irina Higgins

Nat. Mach. Intell., 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, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J. Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

CoRR, 2021

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision.

Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Which priors matter? Benchmarking models for learning latent dynamics.

Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 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

Proceedings of the IEEE International Conference on Robotics and Automation, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Disentangling by Subspace Diffusion.

David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Hamiltonian Generative Networks.

Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

Unsupervised Model Selection for Variational Disentangled Representation Learning.

Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Disentangled Cumulants Help Successor Representations Transfer to New Tasks.

Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh

CoRR, 2019

Equivariant Hamiltonian Flows.

Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth

CoRR, 2019

A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning.

Sunny Duan, Nicholas Watters, Loic Matthey, Chris Burgess, Alexander Lerchner, Irina Higgins

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

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

Towards a Definition of Disentangled Representations.

Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner

CoRR, 2018

Understanding disentangling in β-VAE.

Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

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

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

Proceedings of the 34th International Conference on Machine Learning, 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

Proceedings of the 5th International Conference on Learning Representations, 2017

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

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

2012

Learning view invariant recognition with partially occluded objects.

James M. Tromans, Irina Higgins, Simon M. Stringer

Frontiers Comput. Neurosci., 2012