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2020 – today
- 2023
[j2]Charlie Nash, João Carreira, Jacob C. Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter W. Battaglia:
Transframer: Arbitrary Frame Prediction with Generative Models. Trans. Mach. Learn. Res. 2023 (2023)- 2022
- [c7]Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel:
General-purpose, long-context autoregressive modeling with Perceiver AR. ICML 2022: 8535-8558 - [i10]Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel:
General-purpose, long-context autoregressive modeling with Perceiver AR. CoRR abs/2202.07765 (2022) - [i9]Charlie Nash, João Carreira, Jacob C. Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter W. Battaglia:
Transframer: Arbitrary Frame Prediction with Generative Models. CoRR abs/2203.09494 (2022) - 2021
- [c6]Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov:
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps. CVPR 2021: 4227-4236 - [c5]Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia:
Generating images with sparse representations. ICML 2021: 7958-7968 - [i8]Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia:
Generating Images with Sparse Representations. CoRR abs/2103.03841 (2021) - [i7]Sander Dieleman, Charlie Nash, Jesse H. Engel, Karen Simonyan:
Variable-rate discrete representation learning. CoRR abs/2103.06089 (2021) - [i6]Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov:
HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps. CoRR abs/2106.14880 (2021) - 2020
- [b1]Charlie Nash:
Unsupervised learning with neural latent variable models. University of Edinburgh, UK, 2020 - [c4]Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia:
PolyGen: An Autoregressive Generative Model of 3D Meshes. ICML 2020: 7220-7229 - [i5]Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia:
PolyGen: An Autoregressive Generative Model of 3D Meshes. CoRR abs/2002.10880 (2020)
2010 – 2019
- 2019
- [c3]Charlie Nash, Nate Kushman, Christopher K. I. Williams:
Inverting Supervised Representations with Autoregressive Neural Density Models. AISTATS 2019: 1620-1629 - [c2]Conor Durkan, Charlie Nash:
Autoregressive Energy Machines. ICML 2019: 1735-1744 - [i4]Charlie Nash, Conor Durkan:
Autoregressive Energy Machines. CoRR abs/1904.05626 (2019) - [i3]Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel:
Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR abs/1910.00760 (2019) - 2018
- [i2]Christopher K. I. Williams, Charlie Nash:
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case. CoRR abs/1801.03851 (2018) - [i1]Charlie Nash, Nate Kushman, Christopher K. I. Williams:
Inverting Supervised Representations with Autoregressive Neural Density Models. CoRR abs/1806.00400 (2018) - 2017
- [j1]Charlie Nash, Christopher K. I. Williams:
The shape variational autoencoder: A deep generative model of part-segmented 3D objects. Comput. Graph. Forum 36(5): 1-12 (2017) - 2016
- [c1]Pol Moreno, Christopher K. I. Williams, Charlie Nash, Pushmeet Kohli:
Overcoming Occlusion with Inverse Graphics. ECCV Workshops (3) 2016: 170-185
Coauthor Index
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