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2020 – today
- 2023
[c12]Antonia Creswell, Murray Shanahan, Irina Higgins:
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. ICLR 2023- 2022
- [j5]Irina Higgins, Sébastien Racanière, Danilo J. Rezende:
Symmetry-Based Representations for Artificial and Biological General Intelligence. Frontiers Comput. Neurosci. 16: 836498 (2022) - [i20]Irina Higgins, Sébastien Racanière, Danilo J. Rezende:
Symmetry-Based Representations for Artificial and Biological General Intelligence. CoRR abs/2203.09250 (2022) - [i19]Antonia Creswell, Murray Shanahan, Irina Higgins:
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. CoRR abs/2205.09712 (2022) - [i18]Jonathan Uesato, Nate Kushman, Ramana Kumar, H. Francis Song, Noah Y. Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, Irina Higgins:
Solving math word problems with process- and outcome-based feedback. CoRR abs/2211.14275 (2022) - 2021
- [j4]Irina Higgins
:
Generalizing universal function approximators. Nat. Mach. Intell. 3(3): 192-193 (2021) - [c11]Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, Greg Hajcak:
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. ICLR 2021 - [c10]Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller:
Representation Matters: Improving Perception and Exploration for Robotics. ICRA 2021: 6512-6519 - [c9]Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins:
Which priors matter? Benchmarking models for learning latent dynamics. NeurIPS Datasets and Benchmarks 2021 - [c8]Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev:
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. NeurIPS 2021: 25591-25605 - [i17]Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins:
Which priors matter? Benchmarking models for learning latent dynamics. CoRR abs/2111.05458 (2021) - [i16]Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev:
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. CoRR abs/2111.05986 (2021) - [i15]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:
Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR abs/2112.11446 (2021) - 2020
- [c7]Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins:
Unsupervised Model Selection for Variational Disentangled Representation Learning. ICLR 2020 - [c6]Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins:
Hamiltonian Generative Networks. ICLR 2020 - [c5]David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière:
Disentangling by Subspace Diffusion. NeurIPS 2020 - [i14]David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière:
Disentangling by Subspace Diffusion. CoRR abs/2006.12982 (2020) - [i13]Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Pramod Gupta, Julia Klawohn, Greg Hajcak:
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. CoRR abs/2010.15274 (2020) - [i12]Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller:
Representation Matters: Improving Perception and Exploration for Robotics. CoRR abs/2011.01758 (2020)
2010 – 2019
- 2019
- [i11]Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner:
MONet: Unsupervised Scene Decomposition and Representation. CoRR abs/1901.11390 (2019) - [i10]Sunny Duan, Nicholas Watters, Loic Matthey, Chris Burgess, Alexander Lerchner, Irina Higgins:
A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. CoRR abs/1905.12614 (2019) - [i9]Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth:
Equivariant Hamiltonian Flows. CoRR abs/1909.13739 (2019) - [i8]Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins:
Hamiltonian Generative Networks. CoRR abs/1909.13789 (2019) - [i7]Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh:
Disentangled Cumulants Help Successor Representations Transfer to New Tasks. CoRR abs/1911.10866 (2019) - 2018
- [j3]Irina Higgins, Simon M. Stringer, Jan W. H. Schnupp:
A Computational Account of the Role of Cochlear Nucleus and Inferior Colliculus in Stabilizing Auditory Nerve Firing for Auditory Category Learning. Neural Comput. 30(7) (2018) - [c4]Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) 2018 - [c3]Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins:
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. NeurIPS 2018: 9895-9905 - [i6]Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner:
Understanding disentangling in β-VAE. CoRR abs/1804.03599 (2018) - [i5]Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner, Irina Higgins:
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. CoRR abs/1808.06508 (2018) - [i4]Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner:
Towards a Definition of Disentangled Representations. CoRR abs/1812.02230 (2018) - 2017
- [c2]Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner:
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) 2017 - [c1]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML 2017: 1480-1490 - [i3]Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR abs/1707.03389 (2017) - [i2]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR abs/1707.08475 (2017) - 2016
- [j2]Nasir Ahmad, Irina Higgins, Kerry M. M. Walker, Simon M. Stringer:
Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain. Frontiers Comput. Neurosci. 10: 24 (2016) - [i1]Irina Higgins, Loïc Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner:
Early Visual Concept Learning with Unsupervised Deep Learning. CoRR abs/1606.05579 (2016) - 2012
- [j1]James M. Tromans, Irina Higgins, Simon M. Stringer:
Learning view invariant recognition with partially occluded objects. Frontiers Comput. Neurosci. 6: 48 (2012)
Coauthor Index
aka: Christopher P. Burgess
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