Yuandong Tian

Research Scientist

I am a Research Scientist in Facebook Artificial Intelligence Research since Jan 2015. Before joining FAIR, I was a researcher/SWE in Google X, Self-driving Car team from Sep. 2013 to Jan. 2015. I received PhD in Robotics from Carnegie Mellon University in 2013 and B.S and M.S of Computer Science from Shanghai Jiao Tong University in 2005 and 2008. I am a recipient of ICCV 2013 Marr Prize Honorable Mentions for a hierarchical framework that gives globally optimal guarantees for non-convex non-rigid image deformation, and 2011 Microsoft Research PhD Fellowship.


Computer vision, machine Learning

Latest Publications

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, Peter Vajda, Joseph E. Gonzalez

CVPR - June 1, 2020

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers

Ari Morcos, Haonan Yu, Michela Paganini, Yuandong Tian

NeurIPS - December 10, 2019

Learning to Perform Local Rewriting for Combinatorial Optimization

Xinyun Chen, Yuandong Tian

NeurIPS - December 8, 2019

Hierarchical Decision Making by Generating and Following Natural Language Instructions

Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis

NeurIPS - December 2, 2019

CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication

Jin-Hwa Kim, Nikita Kitaev, Xinlei Chen, Marcus Rohrbach, Byoung-Tak Zhang, Yuandong Tian, Dhruv Batra, Devi Parikh

ACL - July 28, 2019

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

CVPR - June 15, 2019

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick

ICML - June 11, 2019

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma

ICLR - May 6, 2019

M3RL: Mind-Aware Multi-Agent Management Reinforcement Learning

Tianmin Shu, Yuandong Tian

ICLR - May 3, 2019

Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabás Póczos, Aarti Singh

ICML 2018 - July 10, 2018

When is a Convolutional Filter Easy to Learn?

Simon S. Du, Jason D. Lee, Yuandong Tian

ICLR 2018 - April 30, 2018

Building Generalizable Agents with a Realistic and Rich 3D Environment

Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian

ICLR 2018 - April 30, 2018

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick

NIPS 2017 - December 4, 2017

Semantic Amodal Segmentation

Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollar

CVPR 2017 - July 21, 2017

Single Image 3D Interpreter Network

Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman

ECCV 2016 - July 25, 2016

Simple Bag-of-Words Baseline for Visual Question Answering

Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus

ArXiv - December 7, 2015

Scale-Invariant Learning and Convolutional Networks

Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba

ArXiv - June 25, 2015