Charlie Nash
Charlie Nash
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Cited by
Cited by
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
Efficient graph generation with graph recurrent attention networks
R Liao, Y Li, Y Song, S Wang, W Hamilton, DK Duvenaud, R Urtasun, ...
Advances in neural information processing systems 32, 2019
Polygen: An autoregressive generative model of 3d meshes
C Nash, Y Ganin, SMA Eslami, P Battaglia
International conference on machine learning, 7220-7229, 2020
Relational inductive biases, deep learning, and graph networks. arXiv 2018
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
The shape variational autoencoder: A deep generative model of part‐segmented 3d objects
C Nash, CKI Williams
Computer Graphics Forum 36 (5), 1-12, 2017
Generating images with sparse representations
C Nash, J Menick, S Dieleman, PW Battaglia
arXiv preprint arXiv:2103.03841, 2021
Autoregressive energy machines
C Nash, C Durkan
International Conference on Machine Learning, 1735-1744, 2019
General-purpose, long-context autoregressive modeling with Perceiver AR
C Hawthorne, A Jaegle, C Cangea, S Borgeaud, C Nash, M Malinowski, ...
International Conference on Machine Learning, 8535-8558, 2022
Hdmapgen: A hierarchical graph generative model of high definition maps
L Mi, H Zhao, C Nash, X Jin, J Gao, C Sun, C Schmid, N Shavit, Y Chai, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
Overcoming occlusion with inverse graphics
P Moreno, CKI Williams, C Nash, P Kohli
Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
Transframer: Arbitrary frame prediction with generative models
C Nash, J Carreira, J Walker, I Barr, A Jaegle, M Malinowski, P Battaglia
arXiv preprint arXiv:2203.09494, 2022
Inverting supervised representations with autoregressive neural density models
C Nash, N Kushman, CKI Williams
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
The multi-entity variational autoencoder
C Nash, SMA Eslami, C Burgess, I Higgins, D Zoran, T Weber, P Battaglia
NIPS Workshops, 2017
Variable-rate discrete representation learning
S Dieleman, C Nash, J Engel, K Simonyan
arXiv preprint arXiv:2103.06089, 2021
Autoencoders and probabilistic inference with missing data: An exact solution for the factor analysis case
CKI Williams, C Nash, A Nazábal
arXiv preprint arXiv:1801.03851, 2018
Create data from random noise with generative adversarial networks
C Nash
Toptal Engineering Blog, 2017
Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions
C Nash, S Nowozin, N Kushman
Generative models of part-structured 3D objects
C Nash, CK Williams
Predicting protein amino acid sequences using generative models conditioned on protein structure embeddings
AW Senior, S Kohl, J Yim, RJ Bates, CD Ionescu, CTC Nash, ...
US Patent App. 18/275,933, 2024
Generating images using sparse representations
CTC Nash, PW Battaglia
US Patent App. 18/275,048, 2024
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