Alexander Lerchner

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 29

Top Topics

Variational Inference

Leave One Out Cross Validation

Object Representations

Visual Concepts

Top Venues

Publications

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 (2024)
Rishabh Kabra, Loic Matthey, Alexander Lerchner, Niloy J. Mitra
Evaluating VLMs for Score-Based, Multi-Probe Annotation of 3D Objects. CoRR (2023)
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 (2023)
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
Reasoning-Modulated Representations. LoG (2022)
Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo J. Rezende
PARTS: Unsupervised segmentation with slots, attention and independence maximization. ICCV (2021)
Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman, Richard Tanburn, Misha Dashevskiy, Matko Bosnjak
Formalising Concepts as Grounded Abstractions. CoRR (2021)
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)
Petar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell
Reasoning-Modulated Representations. CoRR (2021)
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 Botvinick
Alchemy: A structured task distribution for meta-reinforcement learning. CoRR (2021)
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, Christopher P. Burgess
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR (2021)
James C. R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner
Constellation: Learning relational abstractions over objects for compositional imagination. CoRR (2021)
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)
Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Christopher Burgess, Alexander Lerchner, Irina Higgins
Unsupervised Model Selection for Variational Disentangled Representation Learning. ICLR (2020)
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 (2019)
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner
Multi-Object Representation Learning with Iterative Variational Inference. ICML (2019)
Nicholas Watters, Loïc Matthey, Christopher P. Burgess, Alexander Lerchner
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs. CoRR (2019)
Sunny Duan, Nicholas Watters, Loic Matthey, Christopher P. Burgess, Alexander Lerchner, Irina Higgins
A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning. CoRR (2019)
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loïc Matthey, Matthew Botvinick, Alexander Lerchner
Multi-Object Representation Learning with Iterative Variational Inference. CoRR (2019)
Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew Botvinick, Alexander Lerchner
MONet: Unsupervised Scene Decomposition and Representation. CoRR (2019)
Irina Higgins, David Amos, David Pfau, Sébastien Racanière, Loïc Matthey, Danilo J. Rezende, Alexander Lerchner
Towards a Definition of Disentangled Representations. CoRR (2018)
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) (2018)
Christopher P. Burgess, Irina Higgins, Arka Pal, Loïc Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner
Understanding disentangling in β-VAE. CoRR (2018)
Alessandro Achille, Tom Eccles, Loïc Matthey, Christopher Burgess, Nicholas Watters, Alexander Lerchner, Irina Higgins
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies. NeurIPS (2018)
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 (2018)
Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR (2017)
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) (2017)
Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher Burgess, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR (2017)
Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML (2017)
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 (2016)
Alexander Lerchner, Cristina Ursta, John Hertz, Mandana Ahmadi, Pauline Ruffiot, Søren Enemark
Response Variability in Balanced Cortical Networks. Neural Comput. 18 (3) (2006)
Alexander Lerchner, John Rinzel
Synaptic model for spontaneous activity in developing networks. Neurocomputing (2005)
Alexander Lerchner, Mandana Ahmadi, John Hertz
High-conductance states in a mean-field cortical network model. Neurocomputing (2004)
John Hertz, Alexander Lerchner, Mandana Ahmadi
Mean Field Methods for Cortical Network Dynamics. Summer School on Neural Networks (2003)