Alexander Lerchner
Alexander Lerchner
Research Scientist, DeepMind
Verified email at
Cited by
Cited by
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework.
I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
Iclr 2 (5), 6, 2017
Understanding disentangling in -VAE
CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
Darla: Improving zero-shot transfer in reinforcement learning
I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Early visual concept learning with unsupervised deep learning
I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
dsprites: Disentanglement testing sprites dataset
L Matthey, I Higgins, D Hassabis, A Lerchner
URL https://github. com/deepmind/dsprites-dataset/.[Accessed on: 2018-05-08], 2017
Towards a definition of disentangled representations
I Higgins, D Amos, D Pfau, S Racaniere, L Matthey, D Rezende, ...
arXiv preprint arXiv:1812.02230, 2018
Monet: Unsupervised scene decomposition and representation
CP Burgess, L Matthey, N Watters, R Kabra, I Higgins, M Botvinick, ...
arXiv preprint arXiv:1901.11390, 2019
Response variability in balanced cortical networks
A Lerchner, C Ursta, J Hertz, M Ahmadi, P Ruffiot, S Enemark
Neural computation 18 (3), 634-659, 2006
Multi-object representation learning with iterative variational inference
K Greff, RL Kaufmann, R Kabra, N Watters, C Burgess, D Zoran, L Matthey, ...
arXiv preprint arXiv:1903.00450, 2019
Scan: Learning hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
Scan: learning abstract hierarchical compositional visual concepts
I Higgins, N Sonnerat, L Matthey, A Pal, C Burgess, M Botvinick, ...
stat 1050, 11, 2017
Life-long disentangled representation learning with cross-domain latent homologies
A Achille, T Eccles, L Matthey, C Burgess, N Watters, A Lerchner, ...
Advances in Neural Information Processing Systems, 9873-9883, 2018
Mean field theory for a balanced hypercolumn model of orientation selectivity in primary visual cortex
A Lerchner, G Sterner, J Hertz, M Ahmadi
Network: Computation in Neural Systems 17 (2), 131-150, 2006
High-conductance states in a mean-field cortical network model
A Lerchner, M Ahmadi, J Hertz
Neurocomputing 58, 935-940, 2004
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
N Watters, L Matthey, CP Burgess, A Lerchner
arXiv preprint arXiv:1901.07017, 2019
Knowing without doing
A Lerchner, G La Camera, B Richmond
Nature neuroscience 10 (1), 15-17, 2007
Mean field methods for cortical network dynamics
J Hertz, A Lerchner, M Ahmadi
International School on Neural Networks, Initiated by IIASS and EMFCSC, 71-89, 2003
Cobra: Data-efficient model-based rl through unsupervised object discovery and curiosity-driven exploration
N Watters, L Matthey, M Bosnjak, CP Burgess, A Lerchner
arXiv preprint arXiv:1905.09275, 2019
A unifying framework for understanding state-dependent network dynamics in cortex
A Lerchner, PE Latham
arXiv preprint arXiv:1511.00411, 2015
Training variational autoencoders to generate disentangled latent factors
L Matthey-de-l'Endroit, AT Pal, S Mohamed, X Glorot, I Higgins, ...
US Patent 10,373,055, 2019
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