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Higgins, Irina
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1

Symmetry-Based Representations for Artificial and Biologica..:

Higgins, Irina ; Racanière, Sébastien ; Rezende, Danilo
Frontiers in Computational Neuroscience.  16 (2022)  - p. , 2022
Link: https://doi.org/10.3389/..
 
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2

Generalizing universal function approximators:

Higgins, Irina
Nature Machine Intelligence.  3 (2021)  3 - p. 192-193 , 2021
Link: https://doi.org/10.1038/..
 
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3

Unsupervised deep learning identifies semantic disentanglem..:

Higgins, Irina ; Chang, Le ; Langston, Victoria...
Nature Communications.  12 (2021)  1 - p. , 2021
Link: https://doi.org/10.1038/..
 
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4

Unsupervised learning of temporal features for word categor..:

Higgins, Irina ; Stringer, Simon ; Schnupp, Jan.
PLOS ONE.  12 (2017)  8 - p. e0180174 , 2017
Link: https://doi.org/10.1371/..
 
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5

Harmonic Training and the Formation of Pitch Representation..:

Ahmad, Nasir ; Higgins, Irina ; Walker, Kerry M. M..
Frontiers in Computational Neuroscience.  10 (2016)  - p. , 2016
Link: https://doi.org/10.3389/..
 
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6

Learning view invariant recognition with partially occluded..:

Tromans, James M. ; Higgins, Irina ; Stringer, Simon M.
Frontiers in Computational Neuroscience.  6 (2012)  - p. , 2012
Link: https://doi.org/10.3389/..
 
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7

Symmetry-Based Representations for Artificial and Biologica..:

Higgins, Irina ; Racanière, Sébastien ; Rezende, Danilo
http://arxiv.org/abs/2203.09250.  , 2022
Link: http://arxiv.org/abs/220..
 
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8

Selection-Inference: Exploiting Large Language Models for I..:

Creswell, Antonia ; Shanahan, Murray ; Higgins, Irina
http://arxiv.org/abs/2205.09712.  , 2022
Link: http://arxiv.org/abs/220..
 
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9

Solving math word problems with process- and outcome-based ..:

Uesato, Jonathan ; Kushman, Nate ; Kumar, Ramana...
http://arxiv.org/abs/2211.14275.  , 2022
Link: http://arxiv.org/abs/221..
 
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10

Symmetry-Based Representations for Artificial and Biologica..:

Higgins, Irina ; Racanière, Sébastien ; Rezende, Danilo
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049963/.  , 2022
Link: http://www.ncbi.nlm.nih...
 
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11

Scaling Language Models: Methods, Analysis & Insights from ..:

Rae, Jack W ; Borgeaud, Sebastian ; Cai, Trevor...
http://arxiv.org/abs/2112.11446.  , 2021
Link: http://arxiv.org/abs/211..
 
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12

Unsupervised deep learning identifies semantic disentanglem..:

Higgins, Irina ; Chang, Le ; Langston, Victoria...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578601/.  , 2021
Link: http://www.ncbi.nlm.nih...
 
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13

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynam..:

Higgins, Irina ; Wirnsberger, Peter ; Jaegle, Andrew.
http://arxiv.org/abs/2111.05986.  , 2021
Link: http://arxiv.org/abs/211..
 
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14

Unsupervised deep learning identifies semantic disentanglem..:

Higgins, Irina ; Chang, Le ; Langston, Victoria...
https://arxiv.org/abs/2006.14304.  , 2021
Link: https://doi.org/10.1038/..
 
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15

Which priors matter? Benchmarking models for learning laten..:

Botev, Aleksandar ; Jaegle, Andrew ; Wirnsberger, Peter..
http://arxiv.org/abs/2111.05458.  , 2021
Link: http://arxiv.org/abs/211..
 
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