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
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)
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun (ICML 2018, spotlight)