Charlie Nash

According to our database, Charlie Nash authored at least 13 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Transframer: Arbitrary Frame Prediction with Generative Models.

Charlie Nash, João Carreira, Jacob C. Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter W. Battaglia

Trans. Mach. Learn. Res., 2023

2022

General-purpose, long-context autoregressive modeling with Perceiver AR.

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

Proceedings of the International Conference on Machine Learning, 2022

2021

Variable-rate discrete representation learning.

Sander Dieleman, Charlie Nash, Jesse H. Engel, Karen Simonyan

CoRR, 2021

Generating images with sparse representations.

Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia

Proceedings of the 38th International Conference on Machine Learning, 2021

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps.

Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Unsupervised learning with neural latent variable models.

Charlie Nash

PhD thesis, 2020

PolyGen: An Autoregressive Generative Model of 3D Meshes.

Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Efficient Graph Generation with Graph Recurrent Attention Networks.

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel

CoRR, 2019

Autoregressive Energy Machines.

Conor Durkan, Charlie Nash

Proceedings of the 36th International Conference on Machine Learning, 2019

Inverting Supervised Representations with Autoregressive Neural Density Models.

Charlie Nash, Nate Kushman, Christopher K. I. Williams

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case.

Christopher K. I. Williams, Charlie Nash

CoRR, 2018

2017

The shape variational autoencoder: A deep generative model of part-segmented 3D objects.

Charlie Nash, Christopher K. I. Williams

Comput. Graph. Forum, 2017

2016

Overcoming Occlusion with Inverse Graphics.

Pol Moreno, Christopher K. I. Williams, Charlie Nash, Pushmeet Kohli

Proceedings of the Computer Vision - ECCV 2016 Workshops, 2016