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- affiliation: Carnegie Mellon University
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2020 – today
- 2023
[c69]Taoan Huang
, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Local Branching Relaxation Heuristics for Integer Linear Programs. CPAIOR 2023: 96-113- [c68]Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh:
AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks. HPCA 2023: 317-332 - [i72]Andrew Cohen, Weiping Dou, Jiang Zhu, Slawomir Koziel, Peter Renner, Jan-Ove Mattsson, Xiaomeng Yang, Beidi Chen, Kevin Stone, Yuandong Tian:
Modeling Scattering Coefficients using Self-Attentive Complex Polynomials with Image-based Representation. CoRR abs/2301.02747 (2023) - [i71]Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian:
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. CoRR abs/2301.05104 (2023) - [i70]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. CoRR abs/2302.01578 (2023) - [i69]Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - [i68]Daochen Zha, Louis Feng, Liang Luo, Bhargav Bhushanam, Zirui Liu, Yusuo Hu, Jade Nie, Yuzhen Huang, Yuandong Tian, Arun Kejariwal, Xia Hu:
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models. CoRR abs/2305.01868 (2023) - 2022
- [j8]Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian:
Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5503-5515 (2022) - [c67]Cheng Fu, Hanxian Huang, Bram Wasti, Chris Cummins, Riyadh Baghdadi, Kim M. Hazelwood, Yuandong Tian, Jishen Zhao, Hugh Leather:
Q-gym: An Equality Saturation Framework for DNN Inference Exploiting Weight Repetition. PACT 2022: 291-303 - [c66]Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao:
Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and the Explanations. AAAI 2022: 8267-8276 - [c65]Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. AISTATS 2022: 2736-2761 - [c64]Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather:
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research. CGO 2022: 92-105 - [c63]Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen:
On the Importance of Asymmetry for Siamese Representation Learning. CVPR 2022: 16549-16558 - [c62]Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein:
Re3: Generating Longer Stories With Recursive Reprompting and Revision. EMNLP 2022: 4393-4479 - [c61]Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra:
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training. ICLR 2022 - [c60]Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian:
Understanding Dimensional Collapse in Contrastive Self-supervised Learning. ICLR 2022 - [c59]Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian:
Multi-objective Optimization by Learning Space Partition. ICLR 2022 - [c58]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. ICML 2022: 22591-22612 - [c57]Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu:
AutoShard: Automated Embedding Table Sharding for Recommender Systems. KDD 2022: 4461-4471 - [c56]Yuandong Tian:
Understanding Deep Contrastive Learning via Coordinate-wise Optimization. NeurIPS 2022 - [c55]Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu:
DreamShard: Generalizable Embedding Table Placement for Recommender Systems. NeurIPS 2022 - [i67]Yuandong Tian:
Deep Contrastive Learning is Provably (almost) Principal Component Analysis. CoRR abs/2201.12680 (2022) - [i66]Runlong Zhou, Yuandong Tian, Yi Wu, Simon S. Du:
Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems. CoRR abs/2202.05423 (2022) - [i65]Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen:
On the Importance of Asymmetry for Siamese Representation Learning. CoRR abs/2204.00613 (2022) - [i64]Yuandong Tian:
Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning. CoRR abs/2206.01342 (2022) - [i63]Tongzhou Wang, Simon S. Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian:
Denoised MDPs: Learning World Models Better Than the World Itself. CoRR abs/2206.15477 (2022) - [i62]Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu:
AutoShard: Automated Embedding Table Sharding for Recommender Systems. CoRR abs/2208.06399 (2022) - [i61]Mulong Luo, Wenjie Xiong, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien-Hsin S. Lee, G. Edward Suh:
AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks. CoRR abs/2208.08025 (2022) - [i60]Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian:
Efficient Planning in a Compact Latent Action Space. CoRR abs/2208.10291 (2022) - [i59]Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu:
DreamShard: Generalizable Embedding Table Placement for Recommender Systems. CoRR abs/2210.02023 (2022) - [i58]Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein:
Re3: Generating Longer Stories With Recursive Reprompting and Revision. CoRR abs/2210.06774 (2022) - [i57]Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems. CoRR abs/2210.12547 (2022) - [i56]Minghao Xu, Yuanfan Guo, Yi Xu, Jian Tang, Xinlei Chen, Yuandong Tian:
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data. CoRR abs/2211.12941 (2022) - [i55]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Local Branching Relaxation Heuristics for Integer Linear Programs. CoRR abs/2212.08183 (2022) - [i54]Kevin Yang, Dan Klein, Nanyun Peng, Yuandong Tian:
DOC: Improving Long Story Coherence With Detailed Outline Control. CoRR abs/2212.10077 (2022) - 2021
- [j7]Tianyu Li, Roberto Calandra, Deepak Pathak, Yuandong Tian, Franziska Meier, Akshara Rai:
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion. IEEE Robotics Autom. Lett. 6(2): 2682-2689 (2021) - [c54]Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian:
Understanding Robustness in Teacher-Student Setting: A New Perspective. AISTATS 2021: 3313-3321 - [c53]Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli:
FP-NAS: Fast Probabilistic Neural Architecture Search. CVPR 2021: 15139-15148 - [c52]Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph E. Gonzalez:
FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining. CVPR 2021: 16276-16285 - [c51]Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao:
Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing. ICML 2021: 3469-3479 - [c50]Yuandong Tian, Xinlei Chen, Surya Ganguli:
Understanding self-supervised learning dynamics without contrastive pairs. ICML 2021: 10268-10278 - [c49]Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo:
Few-Shot Neural Architecture Search. ICML 2021: 12707-12718 - [c48]Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. NeurIPS 2021: 378-391 - [c47]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph E. Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. NeurIPS 2021: 9663-9680 - [c46]Xinyun Chen, Dawn Song, Yuandong Tian:
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages. NeurIPS 2021: 22196-22208 - [c45]Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian:
NovelD: A Simple yet Effective Exploration Criterion. NeurIPS 2021: 25217-25230 - [c44]Hang Zhu, Varun Gupta, Satyajeet Singh Ahuja, Yuandong Tian, Ying Zhang, Xin Jin:
Network planning with deep reinforcement learning. SIGCOMM 2021: 258-271 - [i53]Yuandong Tian, Xinlei Chen, Surya Ganguli:
Understanding self-supervised Learning Dynamics without Contrastive Pairs. CoRR abs/2102.06810 (2021) - [i52]Yulai Zhao, Yuandong Tian, Jason D. Lee, Simon S. Du:
Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games. CoRR abs/2102.08903 (2021) - [i51]Zhuolin Yang, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian:
Understanding Robustness in Teacher-Student Setting: A New Perspective. CoRR abs/2102.13170 (2021) - [i50]Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell:
MADE: Exploration via Maximizing Deviation from Explored Regions. CoRR abs/2106.10268 (2021) - [i49]Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. CoRR abs/2106.10544 (2021) - [i48]Xinyun Chen, Dawn Song, Yuandong Tian:
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages. CoRR abs/2107.00101 (2021) - [i47]Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather:
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research. CoRR abs/2109.08267 (2021) - [i46]Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian:
Multi-objective Optimization by Learning Space Partitions. CoRR abs/2110.03173 (2021) - [i45]Xiang Wang, Xinlei Chen, Simon S. Du, Yuandong Tian:
Towards Demystifying Representation Learning with Non-contrastive Self-supervision. CoRR abs/2110.04947 (2021) - [i44]Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian:
Understanding Dimensional Collapse in Contrastive Self-supervised Learning. CoRR abs/2110.09348 (2021) - [i43]Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Mehrdad Mahdavi, Chun-cheng Jason Chen:
Dynamic Graph Representation Learning via Graph Transformer Networks. CoRR abs/2111.10447 (2021) - [i42]Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao:
Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and Explanations. CoRR abs/2112.09174 (2021) - 2020
- [j6]Cheng Fu, Huili Chen, Zhenheng Yang, Farinaz Koushanfar, Yuandong Tian, Jishen Zhao:
Enhancing Model Parallelism in Neural Architecture Search for Multidevice System. IEEE Micro 40(5): 46-55 (2020) - [c43]Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca:
Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search. AAAI 2020: 9983-9991 - [c42]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:
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions. CVPR 2020: 12962-12971 - [c41]Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao:
Deep Symbolic Superoptimization Without Human Knowledge. ICLR 2020 - [c40]Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos:
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP. ICLR 2020 - [c39]Yuandong Tian:
Student Specialization in Deep Rectified Networks With Finite Width and Input Dimension. ICML 2020: 9470-9480 - [c38]Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu:
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. KDD 2020: 945-955 - [c37]Yuandong Tian, Qucheng Gong, Yu Jiang:
Joint Policy Search for Multi-agent Collaboration with Imperfect Information. NeurIPS 2020 - [c36]Linnan Wang, Rodrigo Fonseca, Yuandong Tian:
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. NeurIPS 2020 - [i41]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:
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions. CoRR abs/2004.05565 (2020) - [i40]Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph E. Gonzalez:
FBNetV3: Joint Architecture-Recipe Search using Neural Acquisition Function. CoRR abs/2006.02049 (2020) - [i39]Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo:
Few-shot Neural Architecture Search. CoRR abs/2006.06863 (2020) - [i38]Linnan Wang, Rodrigo Fonseca, Yuandong Tian:
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. CoRR abs/2007.00708 (2020) - [i37]Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu:
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction. CoRR abs/2007.06434 (2020) - [i36]Yuandong Tian, Qucheng Gong, Tina Jiang:
Joint Policy Search for Multi-agent Collaboration with Imperfect Information. CoRR abs/2008.06495 (2020) - [i35]Tianyu Li, Roberto Calandra, Deepak Pathak, Yuandong Tian, Franziska Meier, Akshara Rai:
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion. CoRR abs/2008.11867 (2020) - [i34]Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy:
Real-world Video Adaptation with Reinforcement Learning. CoRR abs/2008.12858 (2020) - [i33]Yuandong Tian, Lantao Yu, Xinlei Chen, Surya Ganguli:
Understanding Self-supervised Learning with Dual Deep Networks. CoRR abs/2010.00578 (2020) - [i32]Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian:
Multi-Agent Collaboration via Reward Attribution Decomposition. CoRR abs/2010.08531 (2020) - [i31]Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli:
FP-NAS: Fast Probabilistic Neural Architecture Search. CoRR abs/2011.10949 (2020) - [i30]Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian:
BeBold: Exploration Beyond the Boundary of Explored Regions. CoRR abs/2012.08621 (2020)
2010 – 2019
- 2019
- [c35]Jin-Hwa Kim, Nikita Kitaev, Xinlei Chen, Marcus Rohrbach, Byoung-Tak Zhang, Yuandong Tian, Dhruv Batra, Devi Parikh:
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication. ACL (1) 2019: 6495-6513 - [c34]Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer:
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search. CVPR 2019: 10734-10742 - [c33]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Bayesian Relational Memory for Semantic Visual Navigation. ICCV 2019: 2769-2779 - [c32]Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma:
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees. ICLR (Poster) 2019 - [c31]Tianmin Shu, Yuandong Tian:
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning. ICLR (Poster) 2019 - [c30]Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick:
ELF OpenGo: an analysis and open reimplementation of AlphaZero. ICML 2019: 6244-6253 - [c29]Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao:
Coda: An End-to-End Neural Program Decompiler. NeurIPS 2019: 3703-3714 - [c28]Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. NeurIPS 2019: 4933-4943 - [c27]Xinyun Chen, Yuandong Tian:
Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS 2019: 6278-6289 - [c26]Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis:
Hierarchical Decision Making by Generating and Following Natural Language Instructions. NeurIPS 2019: 10025-10034 - [i29]Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick:
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero. CoRR abs/1902.04522 (2019) - [i28]Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca:
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search. CoRR abs/1903.11059 (2019) - [i27]Yuandong Tian, Tina Jiang, Qucheng Gong, Ari S. Morcos:
Luck Matters: Understanding Training Dynamics of Deep ReLU Networks. CoRR abs/1905.13405 (2019) - [i26]Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis:
Hierarchical Decision Making by Generating and Following Natural Language Instructions. CoRR abs/1906.00744 (2019) - [i25]Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos:
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP. CoRR abs/1906.02768 (2019) - [i24]Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian:
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. CoRR abs/1906.02773 (2019) - [i23]Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian:
Sample-Efficient Neural Architecture Search by Learning Action Space. CoRR abs/1906.06832 (2019) - [i22]Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao:
A Neural-based Program Decompiler. CoRR abs/1906.12029 (2019) - [i21]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Bayesian Relational Memory for Semantic Visual Navigation. CoRR abs/1909.04306 (2019) - [i20]Yuandong Tian:
Over-parameterization as a Catalyst for Better Generalization of Deep ReLU network. CoRR abs/1909.13458 (2019) - 2018
- [j5]Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman:
3D Interpreter Networks for Viewer-Centered Wireframe Modeling. Int. J. Comput. Vis. 126(9): 1009-1026 (2018) - [j4]I-Chen Wu, Chang-Shing Lee, Yuandong Tian, Martin Müller:
Guest Editorial Special Issue on Deep/Reinforcement Learning and Games. IEEE Trans. Games 10(4): 333-335 (2018) - [c25]Simon S. Du, Jason D. Lee, Yuandong Tian:
When is a Convolutional Filter Easy to Learn? ICLR (Poster) 2018 - [c24]Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian:
Building Generalizable Agents with a Realistic and Rich 3D Environment. ICLR (Workshop) 2018 - [c23]Simon S. Du, Jason D. Lee, Yuandong Tian, Aarti Singh, Barnabás Póczos:
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. ICML 2018: 1338-1347 - [c22]Wenling Shang, Kihyuk Sohn, Yuandong Tian:
Channel-Recurrent Autoencoding for Image Modeling. WACV 2018: 1195-1204 - [i19]Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian:
Building Generalizable Agents with a Realistic and Rich 3D Environment. CoRR abs/1801.02209 (2018) - [i18]Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman:
3D Interpreter Networks for Viewer-Centered Wireframe Modeling. CoRR abs/1804.00782 (2018) - [i17]Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma:
Algorithmic Framework for Model-based Reinforcement Learning with Theoretical Guarantees. CoRR abs/1807.03858 (2018) - [i16]Yuandong Tian:
A theoretical framework for deep locally connected ReLU network. CoRR abs/1809.10829 (2018) - [i15]Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian:
Learning and Planning with a Semantic Model. CoRR abs/1809.10842 (2018) - [i14]Tianmin Shu, Yuandong Tian:
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning. CoRR abs/1810.00147 (2018) - [i13]Xinyun Chen, Yuandong Tian:
Learning to Progressively Plan. CoRR abs/1810.00337 (2018) - [i12]Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, Kurt Keutzer:
Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search. CoRR abs/1812.00090 (2018) - [i11]Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer:
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search. CoRR abs/1812.03443 (2018) - 2017
- [c21]Yan Zhu, Yuandong Tian, Dimitris N. Metaxas, Piotr Dollár:
Semantic Amodal Segmentation. CVPR 2017: 3001-3009 - [c20]