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Charlie Nash

6 accepted papers

2022

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

ICML 2022spotlight

Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohibitively expensive to scale to the number of inputs and layers needed…

2021

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

CVPR 2021poster

High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologie…

Cited by 71PDFScholar
2020

PolyGen: An Autoregressive Generative Model of 3D Meshes

ICML 2020poster

Polygon meshes are an efficient representation of 3D geometry, and are of central importance in computer graphics, robotics and games development. Existing learning-based approaches for object synthesis have avoided the challenges of working with 3D meshes, instead using alternative object represent…

2019

Inverting Supervised Representations with Autoregressive Neural Density Models

AISTATS 2019poster

We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative inversion models to express a distribution over input features conditioned on intermediate model representations. Insights…