Marta Garnelo
Marta Garnelo
Research Scientist at DeepMind
Verified email at - Homepage
Cited by
Cited by
Conditional neural processes
M Garnelo, D Rosenbaum, C Maddison, T Ramalho, D Saxton, ...
International conference on machine learning, 1704-1713, 2018
Neural scene representation and rendering
SMA Eslami, D Jimenez Rezende, F Besse, F Viola, AS Morcos, ...
Science 360 (6394), 1204-1210, 2018
Deep unsupervised clustering with gaussian mixture variational autoencoders
N Dilokthanakul, PAM Mediano, M Garnelo, MCH Lee, H Salimbeni, ...
arXiv preprint arXiv:1611.02648, 2016
Neural processes
M Garnelo, J Schwarz, D Rosenbaum, F Viola, DJ Rezende, SM Eslami, ...
arXiv preprint arXiv:1807.01622, 2018
Attentive neural processes
H Kim, A Mnih, J Schwarz, M Garnelo, A Eslami, D Rosenbaum, O Vinyals, ...
arXiv preprint arXiv:1901.05761, 2019
Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma
M Garnelo, A Tan, Z Her, J Yeong, CJ Lim, J Chen, KH Lim, A Weber, ...
Gut 66 (2), 342-351, 2017
Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
M Garnelo, M Shanahan
Current Opinion in Behavioral Sciences 29, 17-23, 2019
Towards deep symbolic reinforcement learning
M Garnelo, K Arulkumaran, M Shanahan
Deep Reinforcement Learning Workshop at the 30th Conference on Neural …, 2016
Open-ended learning in symmetric zero-sum games
D Balduzzi, M Garnelo, Y Bachrach, W Czarnecki, J Perolat, M Jaderberg, ...
International Conference on Machine Learning, 434-443, 2019
Adaptive posterior learning: few-shot learning with a surprise-based memory module
T Ramalho, M Garnelo
arXiv preprint arXiv:1902.02527, 2019
Game Plan: What AI can do for Football, and What Football can do for AI
K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ...
Journal of Artificial Intelligence Research 71, 41-88, 2021
An explicitly relational neural network architecture
M Shanahan, K Nikiforou, A Creswell, C Kaplanis, D Barrett, M Garnelo
International Conference on Machine Learning, 8593-8603, 2020
Inferring a continuous distribution of atom coordinates from cryo-EM images using VAEs
D Rosenbaum, M Garnelo, M Zielinski, C Beattie, E Clancy, A Huber, ...
arXiv preprint arXiv:2106.14108, 2021
Empirical evaluation of neural process objectives
TA Le, H Kim, M Garnelo, D Rosenbaum, J Schwarz, YW Teh
NeurIPS workshop on Bayesian Deep Learning 4, 2018
Consistent generative query networks
A Kumar, SM Eslami, DJ Rezende, M Garnelo, F Viola, E Lockhart, ...
arXiv preprint arXiv:1807.02033, 2018
A neural architecture for designing truthful and efficient auctions
A Tacchetti, DJ Strouse, M Garnelo, T Graepel, Y Bachrach
arXiv preprint arXiv:1907.05181 3 (3.6), 4, 2019
Meta-learning surrogate models for sequential decision making
A Galashov, J Schwarz, H Kim, M Garnelo, D Saxton, P Kohli, SM Eslami, ...
arXiv preprint arXiv:1903.11907, 2019
Verification of deep probabilistic models
K Dvijotham, M Garnelo, A Fawzi, P Kohli
arXiv preprint arXiv:1812.02795, 2018
Pick your battles: Interaction graphs as population-level objectives for strategic diversity
M Garnelo, WM Czarnecki, S Liu, D Tirumala, J Oh, G Gidel, ...
arXiv preprint arXiv:2110.04041, 2021
Game-theoretic vocabulary selection via the shapley value and banzhaf index
R Patel, M Garnelo, I Gemp, C Dyer, Y Bachrach
Proceedings of the 2021 Conference of the North American Chapter of the …, 2021
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