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Transframer: Arbitrary Frame Prediction with Generative Models


Mar 18, 2022
Charlie Nash, João Carreira, Jacob Walker, Iain Barr, Andrew Jaegle, Mateusz Malinowski, Peter Battaglia


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


Feb 15, 2022
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse Engel


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HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps


Jun 28, 2021
Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov


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Variable-rate discrete representation learning


Mar 10, 2021
Sander Dieleman, Charlie Nash, Jesse Engel, Karen Simonyan

* 26 pages, 15 figures, samples can be found at https://vdrl.github.io/ 

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Generating Images with Sparse Representations


Mar 05, 2021
Charlie Nash, Jacob Menick, Sander Dieleman, Peter W. Battaglia


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PolyGen: An Autoregressive Generative Model of 3D Meshes


Feb 23, 2020
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter W. Battaglia


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Efficient Graph Generation with Graph Recurrent Attention Networks


Oct 02, 2019
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel

* Neural Information Processing Systems (NeurIPS) 2019 

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Autoregressive Energy Machines


Apr 11, 2019
Charlie Nash, Conor Durkan


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


Sep 03, 2018
Christopher K. I. Williams, Charlie Nash, Alfredo Nazábal

* 7 pages, 2 figures 

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Inverting Supervised Representations with Autoregressive Neural Density Models


Jun 01, 2018
Charlie Nash, Nate Kushman, Christopher K. I. Williams


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