Aidan Clark
Aidan Clark
Verified email at
Cited by
Cited by
An empirical analysis of compute-optimal large language model training
J Hoffmann, S Borgeaud, A Mensch, E Buchatskaya, T Cai, E Rutherford, ...
Advances in Neural Information Processing Systems 35, 30016-30030, 2022
Scaling language models: Methods, analysis & insights from training gopher
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
Improving language models by retrieving from trillions of tokens
S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ...
International conference on machine learning, 2206-2240, 2022
Skilful precipitation nowcasting using deep generative models of radar
S Ravuri, K Lenc, M Willson, D Kangin, R Lam, P Mirowski, M Fitzsimons, ...
Nature 597 (7878), 672-677, 2021
Stabilizing transformers for reinforcement learning
E Parisotto, F Song, J Rae, R Pascanu, C Gulcehre, S Jayakumar, ...
International conference on machine learning, 7487-7498, 2020
Adversarial video generation on complex datasets
A Clark, J Donahue, K Simonyan
arXiv preprint arXiv:1907.06571, 2019
High fidelity speech synthesis with adversarial networks
M Bińkowski, J Donahue, S Dieleman, A Clark, E Elsen, N Casagrande, ...
arXiv preprint arXiv:1909.11646, 2019
The DeepMind JAX Ecosystem
I Babuschkin, K Baumli, A Bell, S Bhupatiraju, J Bruce, P Buchlovsky, ...
URL http://github. com/deepmind 24, 25, 2020
Unified scaling laws for routed language models
A Clark, D de Las Casas, A Guy, A Mensch, M Paganini, J Hoffmann, ...
International conference on machine learning, 4057-4086, 2022
V-mpo: On-policy maximum a posteriori policy optimization for discrete and continuous control
HF Song, A Abdolmaleki, JT Springenberg, A Clark, H Soyer, JW Rae, ...
arXiv preprint arXiv:1909.12238, 2019
Transformation-based adversarial video prediction on large-scale data
P Luc, A Clark, S Dieleman, DL Casas, Y Doron, A Cassirer, K Simonyan
arXiv preprint arXiv:2003.04035, 2020
TF-Replicator: Distributed machine learning for researchers
P Buchlovsky, D Budden, D Grewe, C Jones, J Aslanides, F Besse, ...
arXiv preprint arXiv:1902.00465, 2019
Podracer architectures for scalable reinforcement learning
M Hessel, M Kroiss, A Clark, I Kemaev, J Quan, T Keck, F Viola, ...
arXiv preprint arXiv:2104.06272, 2021
Unsupervised authorial clustering based on syntactic structure
A Daks, A Clark
Proceedings of the ACL 2016 Student Research Workshop, 114-118, 2016
Generative Adversarial Networks with Temporal and Spatial Discriminators for Efficient Video Generation
A Clark, J Donahue, K Simonyan
US Patent App. 17/613,694, 2022
Recurrent unit for generating or processing a sequence of images
LUC Pauline, A Clark, SEL Dieleman, K Simonyan
US Patent App. 17/797,198, 2023
Training conditional computation neural networks using reinforcement learning
A Clark, A Mensch
US Patent App. 18/076,978, 2023
A Contextual Discretization framework for compressing Recurrent Neural Networks
A Clark, VU Prabhu, J Whaley
Open Library of Bioscience
S Ravuri, K Lenc, M Willson, D Kangin, R Lam, P Mirowski, M Fitzsimons, ...
The system can't perform the operation now. Try again later.
Articles 1–19