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Core AI tech

Areas of fundamental machine learning science include sparse and efficient operations for real-time inference, theoretical analysis of the robustness and safety of neural networks, stochastic and non-linear optimization, learning in the presence of noisy biased data, and more.

Recent ATG R&D publications

Hierarchical Verification for Adversarial Robustness

Cong Han Lim, Raquel Urtasun, Ersin Yumer (ICML 2020)

Conditional Entropy Coding for Efficient Video Compression

Jerry Liu, Shenlong Wang, Wei-Chiu Ma, Meet Shah, Rui Hu, Pranaab Dhawan, Raquel Urtasun (ECCV 2020)

Learning Lane Graph Representations for Motion Forecasting

Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun (ECCV 2020, oral)

LevelSet R-CNN: A Deep Variational Method for Instance Segmentation

Namdar Homayounfar, Yuwen Xiong, Justin Liang, Wei-Chiu Ma, Raquel Urtasun (ECCV 2020)

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun (CVPR 2020, oral)

DSIC: Deep Stereo Image Compression

Jerry Liu, Shenlong Wang, Raquel Urtasun (ICCV 2019, oral)

Graph HyperNetworks for Neural Architecture Search

Chris Zhang, Mengye Ren, Raquel Urtasun (ICLR 2019)

SBNet: Sparse Blocks Network for Fast Inference

Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun (CVPR 2018, spotlight)

Deep Parametric Continuous Convolutional Neural Networks

Shenlong Wang*, Simon Suo*, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun (CVPR 2018, spotlight)

Matching Adversarial Networks

Gellért Máttyus, Raquel Urtasun (CVPR 2018)

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun (ICML 2018, spotlight)

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