Alexander Lerchner

According to our database, Alexander Lerchner authored at least 30 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Can foundation models actively gather information in interactive environments to test hypotheses?

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

CoRR, 2024

Scaling Instructable Agents Across Many Simulated Worlds.

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

CoRR, 2024

Leveraging VLM-Based Pipelines to Annotate 3D Objects.

Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SODA: Bottleneck Diffusion Models for Representation Learning.

Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects.

Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra

CoRR, 2023

2022

Reasoning-Modulated Representations.

Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell

Proceedings of the Learning on Graphs Conference, 2022

2021

Constellation: Learning relational abstractions over objects for compositional imagination.

James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner

CoRR, 2021

Alchemy: A structured task distribution for meta-reinforcement learning.

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

CoRR, 2021

Formalising Concepts as Grounded Abstractions.

Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman, Richard Tanburn, Misha Dashevskiy, Matko Bosnjak

CoRR, 2021

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.

Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents.

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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

PARTS: Unsupervised segmentation with slots, attention and independence maximization.

Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

Unsupervised Model Selection for Variational Disentangled Representation Learning.

Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning.

Sunny Duan, Nicholas Watters, Loic Matthey, Chris Burgess, Alexander Lerchner, Irina Higgins

CoRR, 2019

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration.

Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P. Burgess, Alexander Lerchner

CoRR, 2019

MONet: Unsupervised Scene Decomposition and Representation.

Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner

CoRR, 2019

Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs.

Nicholas Watters, Loïc Matthey, Christopher P. Burgess, Alexander Lerchner

CoRR, 2019

Multi-Object Representation Learning with Iterative Variational Inference.

Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Towards a Definition of Disentangled Representations.

Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner

CoRR, 2018

Understanding disentangling in β-VAE.

Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner

CoRR, 2018

Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.

Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher P. Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

SCAN: Learning Hierarchical Compositional Visual Concepts.

Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

SCAN: Learning Abstract Hierarchical Compositional Visual Concepts.

Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner

CoRR, 2017

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.

Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner

Proceedings of the 34th International Conference on Machine Learning, 2017

beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.

Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Early Visual Concept Learning with Unsupervised Deep Learning.

Irina Higgins, Loïc Matthey, Xavier Glorot, Arka Pal, Benigno Uria, Charles Blundell, Shakir Mohamed, Alexander Lerchner

CoRR, 2016

2006

Response Variability in Balanced Cortical Networks.

Alexander Lerchner, Cristina Ursta, John Hertz, Mandana Ahmadi, Pauline Ruffiot, Søren Enemark

Neural Comput., 2006

2005

Synaptic model for spontaneous activity in developing networks.

Alexander Lerchner, John Rinzel

Neurocomputing, 2005

2004

High-conductance states in a mean-field cortical network model.

Alexander Lerchner, Mandana Ahmadi, John Hertz

Neurocomputing, 2004

2003

Mean Field Methods for Cortical Network Dynamics.

John Hertz, Alexander Lerchner, Mandana Ahmadi

Proceedings of the Computational Neuroscience: Cortical Dynamics, 2003