| 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 | 4024 | 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 | 390 | 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 | 275 | 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, 1806 | 248 | 1806 |
| Generating images with sparse representations C Nash, J Menick, S Dieleman, PW Battaglia arXiv preprint arXiv:2103.03841, 2021 | 195 | 2021 |
| 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 | 147 | 2017 |
| 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 | 70 | 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 | 67 | 2021 |
| Autoregressive energy machines C Nash, C Durkan International Conference on Machine Learning, 1735-1744, 2019 | 66 | 2019 |
| Overcoming occlusion with inverse graphics P Moreno, CKI Williams, C Nash, P Kohli Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 43 | 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 | 39 | 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 | 31 | 2019 |
| The multi-entity variational autoencoder C Nash, SMA Eslami, C Burgess, I Higgins, D Zoran, T Weber, P Battaglia NIPS Workshops, 2017 | 27 | 2017 |
| Variable-rate discrete representation learning S Dieleman, C Nash, J Engel, K Simonyan arXiv preprint arXiv:2103.06089, 2021 | 26 | 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 | 23 | 2018 |
| Create data from random noise with generative adversarial networks C Nash Toptal Engineering Blog, 2017 | 7 | 2017 |
| Generating sequences of data elements using cross-attention operations CGM Hawthorne, AC Jaegle, CC Cangea, SBD Avocat, CTC Nash, ... US Patent App. 18/102,985, 2023 | 1 | 2023 |
| Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions C Nash, S Nowozin, N Kushman | 1 | 2018 |
| Generative models of part-structured 3D objects C Nash, CK Williams | 1 | |
| Autoregressive neural networks for the generation of polygonal meshes of 3D objects CTC Nash, I Ganin, SM Eslami, PW Battaglia US Patent 12,131,243, 2024 | | 2024 |
Peter BattagliaResearch Scientist, DeepMindDirección de correo verificada de google.com