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2020 – today
- 2024
[c13]Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner:
SODA: Bottleneck Diffusion Models for Representation Learning. CVPR 2024: 23115-23127- [c12]Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra:
Leveraging VLM-Based Pipelines to Annotate 3D Objects. ICML 2024 - [i19]SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young:
Scaling Instructable Agents Across Many Simulated Worlds. CoRR abs/2404.10179 (2024) - 2023
- [i18]Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra:
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects. CoRR abs/2311.17851 (2023) - [i17]Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner:
SODA: Bottleneck Diffusion Models for Representation Learning. CoRR abs/2311.17901 (2023) - 2022
- [c11]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. LoG 2022: 50 - 2021
- [c10]Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende:
PARTS: Unsupervised segmentation with slots, attention and independence maximization. ICCV 2021: 10419-10427 - [c9]Jane Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charles Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matt M. Botvinick:
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. NeurIPS Datasets and Benchmarks 2021 - [c8]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. NeurIPS 2021: 20146-20159 - [i16]Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman, Richard Tanburn, Misha Dashevskiy, Matko Bosnjak:
Formalising Concepts as Grounded Abstractions. CoRR abs/2101.05125 (2021) - [i15]Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew M. Botvinick:
Alchemy: A structured task distribution for meta-reinforcement learning. CoRR abs/2102.02926 (2021) - [i14]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR abs/2106.03849 (2021) - [i13]Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell:
Reasoning-Modulated Representations. CoRR abs/2107.08881 (2021) - [i12]James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner:
Constellation: Learning relational abstractions over objects for compositional imagination. CoRR abs/2107.11153 (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
2010 – 2019
- 2019
- [c6]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. ICML 2019: 2424-2433 - [i11]Nicholas Watters, Loïc Matthey, Christopher P. Burgess, Alexander Lerchner:
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs. CoRR abs/1901.07017 (2019) - [i10]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) - [i9]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loïc Matthey, Matthew M. Botvinick, Alexander Lerchner
:
Multi-Object Representation Learning with Iterative Variational Inference. CoRR abs/1903.00450 (2019) - [i8]Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, Alexander Lerchner:
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration. CoRR abs/1905.09275 (2019) - [i7]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) - 2018
- [c5]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 - [c4]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
- [c3]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 - [c2]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
- [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)
2000 – 2009
- 2006
- [j3]Alexander Lerchner, Cristina Ursta, John Hertz, Mandana Ahmadi, Pauline Ruffiot, Søren Enemark:
Response Variability in Balanced Cortical Networks. Neural Comput. 18(3): 634-659 (2006) - 2005
- [j2]Alexander Lerchner, John Rinzel:
Synaptic model for spontaneous activity in developing networks. Neurocomputing 65-66: 777-782 (2005) - 2004
- [j1]Alexander Lerchner, Mandana Ahmadi, John Hertz:
High-conductance states in a mean-field cortical network model. Neurocomputing 58-60: 935-940 (2004) - 2003
- [c1]John Hertz, Alexander Lerchner, Mandana Ahmadi:
Mean Field Methods for Cortical Network Dynamics. Summer School on Neural Networks 2003: 71-89
Coauthor Index
aka: Matthew M. Botvinick
aka: Christopher P. Burgess
aka: Loic Matthey
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last updated on 2024-10-07 01:22 CEST by the dblp team
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