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Joost van Amersfoort
Joost van Amersfoort
Research Scientist, Google DeepMind
Verified email at deepmind.com - Homepage
Title
Cited by
Cited by
Year
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning
A Kirsch, J van Amersfoort, Y Gal
NeurIPS 2019, 2019
5552019
Uncertainty estimation using a single deep deterministic neural network
J van Amersfoort, L Smith, YW Teh, Y Gal
International Conference on Machine Learning, 2020
466*2020
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
4072023
Variational Recurrent Auto-Encoders
O Fabius, J van Amersfoort
ICLR 2015 Workshop, 2014
3512014
Deep deterministic uncertainty: A new simple baseline
J Mukhoti, A Kirsch, J van Amersfoort, PHS Torr, Y Gal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
157*2023
On feature collapse and deep kernel learning for single forward pass uncertainty
J Van Amersfoort, L Smith, A Jesson, O Key, Y Gal
arXiv preprint arXiv:2102.11409, 2021
123*2021
Transformation-based models of video sequences
J van Amersfoort, A Kannan, MA Ranzato, A Szlam, D Tran, S Chintala
arXiv preprint arXiv:1701.08435, 2017
812017
Plex: Towards reliability using pretrained large model extensions
D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ...
arXiv preprint arXiv:2207.07411, 2022
802022
Frame interpolation with multi-scale deep loss functions and generative adversarial networks
J Van Amersfoort, W Shi, A Acosta, F Massa, J Totz, Z Wang, J Caballero
arXiv preprint arXiv:1711.06045, 2017
492017
Prospect pruning: Finding trainable weights at initialization using meta-gradients
M Alizadeh, SA Tailor, LM Zintgraf, J van Amersfoort, S Farquhar, ...
arXiv preprint arXiv:2202.08132, 2022
292022
Deep deterministic uncertainty for semantic segmentation
J Mukhoti, J van Amersfoort, PHS Torr, Y Gal
arXiv preprint arXiv:2111.00079, 2021
222021
Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data
A Jesson, P Tigas, J van Amersfoort, A Kirsch, U Shalit, Y Gal
Advances in Neural Information Processing Systems 34, 30465-30478, 2021
212021
Single shot structured pruning before training
J van Amersfoort, M Alizadeh, S Farquhar, N Lane, Y Gal
arXiv preprint arXiv:2007.00389, 2020
192020
Mixtures of large-scale dynamic functional brain network modes
C Gohil, E Roberts, R Timms, A Skates, C Higgins, A Quinn, U Pervaiz, ...
NeuroImage 263, 119595, 2022
142022
Can convolutional ResNets approximately preserve input distances? A frequency analysis perspective
L Smith, J van Amersfoort, H Huang, S Roberts, Y Gal
arXiv preprint arXiv:2106.02469, 2021
102021
Deep hashing using entropy regularised product quantisation network
J Schlemper, J Caballero, A Aitken, J van Amersfoort
arXiv preprint arXiv:1902.03876, 2019
72019
Frame interpolation with multi-scale deep loss functions and generative adversarial networks
J Van Amersfoort, W Shi, J Caballero, AAA Diaz, F Massa, J Totz, Z Wang
US Patent 11,122,238, 2021
52021
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
42024
Decomposing representations for deterministic uncertainty estimation
H Huang, J van Amersfoort, Y Gal
arXiv preprint arXiv:2112.00856, 2021
32021
DyNeMoC: A semi-supervised architecture for classifying time series brain data
AMS Khan, C Gohil, P Notin, J van Amersfoort, M Woolrich, Y Gal
ICLR 2023 Workshop on Time Series Representation Learning for Health, 2023
2023
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