Alexander Lerchner

38 papers A* 8Journal 25Unranked 5
YearRankTypeTitle / Venue / Authors
2025 J jnl
Exploring exploration with foundation agents in interactive environments.
Trans. Mach. Learn. Res.
Daniel P. Sawyer, Nan Rosemary Ke, Hubert Soyer, Martin Engelcke, John Reid, David P. Reichert, Drew A. Hudson, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P. Lillicrap, Michael Curtis Mozer, Jane X. Wang
2025 J jnl
LayerLock: Non-collapsing Representation Learning with Progressive Freezing.
CoRR
Goker Erdogan, Nikhil Parthasarathy, Catalin Ionescu, Drew A. Hudson, Alexander Lerchner, Andrew Zisserman, Mehdi S. M. Sajjadi, João Carreira
2024 J jnl
Can foundation models actively gather information in interactive environments to test hypotheses?
CoRR
Nan Rosemary Ke, Danny P. Sawyer, Hubert Soyer, Martin Engelcke, David P. Reichert, Drew A. Hudson, John Reid, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P. Lillicrap, Michael Mozer, Jane X. Wang
2024 A* conf
Leveraging VLM-Based Pipelines to Annotate 3D Objects.
ICML
Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra
2024 A* conf
SODA: Bottleneck Diffusion Models for Representation Learning.
CVPR
Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner
2024 J jnl
Scaling Instructable Agents Across Many Simulated Worlds.
CoRR
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
2023 J jnl
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects.
CoRR
Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra
2023 J jnl
SODA: Bottleneck Diffusion Models for Representation Learning.
CoRR
Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner
2022 — conf
Reasoning-Modulated Representations.
LoG
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
2021 — conf
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents.
NeurIPS Datasets and Benchmarks
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
2021 J jnl
Alchemy: A structured task distribution for meta-reinforcement learning.
CoRR
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
2021 J jnl
Constellation: Learning relational abstractions over objects for compositional imagination.
CoRR
James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner
2021 J jnl
Formalising Concepts as Grounded Abstractions.
CoRR
Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman, Richard Tanburn, Misha Dashevskiy, Matko Bosnjak
2021 A* conf
PARTS: Unsupervised segmentation with slots, attention and independence maximization.
ICCV
Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende
2021 J jnl
Reasoning-Modulated Representations.
CoRR
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
2021 A* conf
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.
NeurIPS
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess
2021 J jnl
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.
CoRR
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew M. Botvinick, Alexander Lerchner, Christopher P. Burgess
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
COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration.
CoRR
Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, Alexander Lerchner
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
2019 A* conf
Multi-Object Representation Learning with Iterative Variational Inference.
ICML
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner
2019 J jnl
Multi-Object Representation Learning with Iterative Variational Inference.
CoRR
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loïc Matthey, Matthew M. Botvinick, Alexander Lerchner
2019 J jnl
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs.
CoRR
Nicholas Watters, Loïc Matthey, Christopher P. Burgess, Alexander Lerchner
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
2006 J jnl
Response Variability in Balanced Cortical Networks.
Neural Comput.
Alexander Lerchner, Cristina Ursta, John Hertz, Mandana Ahmadi, Pauline Ruffiot, Søren Enemark
2005 J jnl
Synaptic model for spontaneous activity in developing networks.
Neurocomputing
Alexander Lerchner, John Rinzel
2004 J jnl
High-conductance states in a mean-field cortical network model.
Neurocomputing
Alexander Lerchner, Mandana Ahmadi, John Hertz
2003 — conf
Mean Field Methods for Cortical Network Dynamics.
Summer School on Neural Networks
John Hertz, Alexander Lerchner, Mandana Ahmadi