Irina Higgins - RankMe

37 papers A* 9Journal 25Unranked 3

Year	Rank	Type	Title / Venue / Authors
2023	A*	`conf`	Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. ICLR Antonia Creswell, Murray Shanahan, Irina Higgins
2022	J	`jnl`	Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. CoRR Antonia Creswell, Murray Shanahan, Irina Higgins
2022	J	`jnl`	Solving math word problems with process- and outcome-based feedback. CoRR Jonathan Uesato, Nate Kushman, Ramana Kumar, H. Francis Song, Noah Y. Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, Irina Higgins
2022	J	`jnl`	Symmetry-Based Representations for Artificial and Biological General Intelligence. CoRR Irina Higgins, Sébastien Racanière, Danilo J. Rezende
2022	J	`jnl`	Symmetry-Based Representations for Artificial and Biological General Intelligence. Frontiers Comput. Neurosci. Irina Higgins, Sébastien Racanière, Danilo J. Rezende
2021	J	`jnl`	Generalizing universal function approximators. Nat. Mach. Intell. Irina Higgins
2021	A*	`conf`	Representation Matters: Improving Perception and Exploration for Robotics. ICRA Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller
2021	A*	`conf`	Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. ICLR Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Sunny Duan, Pramod Gupta, Julia Klawohn, Greg Hajcak
2021	J	`jnl`	Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR 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
2021	A*	`conf`	SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. NeurIPS Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
2021	J	`jnl`	SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision. CoRR Irina Higgins, Peter Wirnsberger, Andrew Jaegle, Aleksandar Botev
2021	—	`conf`	Which priors matter? Benchmarking models for learning latent dynamics. NeurIPS Datasets and Benchmarks Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins
2021	J	`jnl`	Which priors matter? Benchmarking models for learning latent dynamics. CoRR Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger, Daniel Hennes, Irina Higgins
2020	A*	`conf`	Disentangling by Subspace Diffusion. NeurIPS David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
2020	J	`jnl`	Disentangling by Subspace Diffusion. CoRR David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière
2020	A*	`conf`	Hamiltonian Generative Networks. ICLR Peter Toth, Danilo J. Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins
2020	J	`jnl`	Representation Matters: Improving Perception and Exploration for Robotics. CoRR Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck, Irina Higgins, Ankush Gupta, Tejas Kulkarni, Malcolm Reynolds, Denis Teplyashin, Roland Hafner, Thomas Lampe, Martin A. Riedmiller
2020	J	`jnl`	Representation learning for improved interpretability and classification accuracy of clinical factors from EEG. CoRR Garrett Honke, Irina Higgins, Nina Thigpen, Vladimir Miskovic, Katie Link, Pramod Gupta, Julia Klawohn, Greg Hajcak
2020	A*	`conf`	Unsupervised Model Selection for Variational Disentangled Representation Learning. ICLR Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins
2019	J	`jnl`	A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. CoRR Sunny Duan, Nicholas Watters, Loic Matthey, Chris Burgess, Alexander Lerchner, Irina Higgins
2019	J	`jnl`	Disentangled Cumulants Help Successor Representations Transfer to New Tasks. CoRR Christopher Grimm, Irina Higgins, André Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh
2019	J	`jnl`	Equivariant Hamiltonian Flows. CoRR Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, Peter Toth
2019	J	`jnl`	Hamiltonian Generative Networks. CoRR Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, Irina Higgins
2019	J	`jnl`	MONet: Unsupervised Scene Decomposition and Representation. CoRR Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner
2018	J	`jnl`	A Computational Account of the Role of Cochlear Nucleus and Inferior Colliculus in Stabilizing Auditory Nerve Firing for Auditory Category Learning. Neural Comput. Irina Higgins, Simon M. Stringer, Jan W. H. Schnupp
2018	A*	`conf`	Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. NeurIPS Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins
2018	J	`jnl`	Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. CoRR Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nick Watters, Alexander Lerchner, Irina Higgins
2018	—	`conf`	SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner
2018	J	`jnl`	Towards a Definition of Disentangled Representations. CoRR Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner
2018	J	`jnl`	Understanding disentangling in β-VAE. CoRR Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner
2017	A*	`conf`	DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner
2017	J	`jnl`	DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner
2017	J	`jnl`	SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner
2017	—	`conf`	beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner
2016	J	`jnl`	Early Visual Concept Learning with Unsupervised Deep Learning. CoRR Irina Higgins, Loïc Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner
2016	J	`jnl`	Harmonic Training and the Formation of Pitch Representation in a Neural Network Model of the Auditory Brain. Frontiers Comput. Neurosci. Nasir Ahmad, Irina Higgins, Kerry M. M. Walker, Simon M. Stringer
2012	J	`jnl`	Learning view invariant recognition with partially occluded objects. Frontiers Comput. Neurosci. James M. Tromans, Irina Higgins, Simon M. Stringer