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News
[Sep. 21, 2023] 3 papers ( ) are accepted in NeurIPS2023!
[May. 02, 2023] 1 papers () is accepted in ACL2023! [Apr. 24, 2023] 4 papers ( ) are accepted in ICML2023! [Mar. 02, 2023] Invited talk in Microsoft on Long-form Story Generation (, ). Slides here. [Feb. 28, 2023] Invited talk in IPAM about AI-guided optimization (, ). Slides here. [Feb. 17, 2023] 1 papers () is accepted in MLSys2023! [Jan. 25, 2023] 1 papers () is accepted in CPAIOR2023! [Jan. 20, 2023] 3 papers ( ) are accepted in ICLR2023! [Oct. 17, 2022] 1 papers () is accepted in HPCA2023! [Oct. 06, 2022] 1 papers () is accepted in EMNLP2022! [Oct. 03, 2022] Invited talk in MIT Poggio's lab about recent works on contrastive learning (, ). Slides here. [Sep. 14, 2022] 2 papers ( ) are accepted in NeurIPS2022! [Sep. 13, 2022] Co-organize AAAI'23 workshop "Reinforcement Learning Ready for Production". [Aug. 21, 2022] Keynote talk in IEEE Conference on Games. [Aug. 04, 2022] Invited talk in TTIC workshop on representation learning theory. Link [Jun. 08, 2022] Invited talk in VALSE Webinar about SSL. [May. 18, 2022] 1 papers () is accepted in KDD2022! [May. 15, 2022] 1 papers () is accepted in ICML2022! [Apr. 28, 2022] Guest lecture in Tianqi Chen's group in CMU. [Apr. 07, 2022] Invited talk in UIUC about representation learning. [Mar. 05, 2022] 1 papers () is accepted in CVPR2022! [Jan. 24, 2022] 3 papers ( ) are accepted in ICLR2022! [Nov. 29, 2021] 1 papers () is accepted in AAAI2022! [Nov. 05, 2021] 1 papers () is accepted in CGO2022! [Sep. 28, 2021] 4 papers ( ) are accepted in NeurIPS2021! [Jul. 19, 2021] Our paper got ICML Outstanding Paper Award Honorable Mention! [Jun. 04, 2021] Invited talk in University of Washington NeuralAI Lab about . Slides here. Thanks Eli Shlizerman for inviting! [May. 08, 2021] 3 papers ( ) are accepted in ICML2021! [Apr. 29, 2021] 1 papers () is accepted in SIGCOMM2021! [Apr. 21, 2021] Invited talk in VALSE Webinar about understanding self-supervised learning. Slides here [Apr. 21, 2021] Invited Guest Lecture in UPenn (Thanks Jing Li) for the invitation. Slides here. [Apr. 12, 2021] Invited Talk in UCL DARK Lab. Slides here. [Feb. 28, 2021] 2 papers ( ) are accepted in CVPR2021! [Jan. 31, 2021] In Black-box optimization challenge of NeurIPS'20, two teams extended our and won 3rd and 8th place! See their reports (JetBrains, KAIST). [Jan. 22, 2021] 1 papers () is accepted in AIStats2021! [Dec. 12, 2020] Invited talk in NeurIPS 2020 workshop of Learning meets Combinatorial Algorithms. [Dec. 12, 2020] Contributed talk in NeurIPS 2020 workshop of Self-supervised Learning, Theory and Practice. [Nov. 30, 2020] Invited talk (Slides) at Workshop of Reinforcement Learning from Batch Data and Simulation in Simons Institute of UC Berkeley. [Oct. 20, 2020] Invited Guest lecture in University of Wisconsin Madison (Class syllabus). [Oct. 14, 2020] Distinguished Guest lecture in IIIS, Tsinghua University. [Jun. 06, 2020] Invited guest lecture in UCLA. [Nov. 07, 2019] Invited talk in IAS "Workshop on New Directions in Reinforcement Learning and Control" in Princeton University. [Nov. 06, 2019] Invited talk in NEC Laboratories Princeton. [Oct. 27, 2019] Invited talk in AI Sys Workshop in SOSP'19 [Jun. 01, 2019] Long oral talk about in ICML 2019. [Jan. 01, 2019] Talks in Deep Learning Summit, AAAI 2019 Workshops (Reproducible AI and Game and Environments in Artificial Intelligence). [Jun. 01, 2018] Multiple talks in Stanford, AI NextCon, etc. link [Dec. 20, 2017] Keynote at Future Leaders of AI Retreat (FLAIR), Shanghai. Slides here. [Dec. 06, 2017] Oral talk about platform, NIPS 2017, Long Beach. Slides link. [Nov. 05, 2017] DRL and Game Tutorial in AI Frontier, Santa Clara. Slides link. [Oct. 27, 2017] DRL and Game Tutorial in Mountain View, ACMMM 2017. Slides link. [Aug. 10, 2017] Presentation in Video Games and Machine Learning VGML Workshop, ICML 2017. Slides here. The same talk is also presented in University of Sydney on Aug. 11, hosted by Dong Xu. [Jul. 01, 2017] On topic "AI In Games: Achievements and Challenges", giving 5 talks in China (CASIA, Tsinghua, Shanghai Tech, Brain-AI workshop and CCF-GAIR 2017) located in Beijing, Shanghai and Shenzhen. Slides here. |
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JoMA: Demystifying Multilayer Transformers via JOint Dynamics of MLP and Attention Yuandong Tian, Yiping Wang, Zhenyu Zhang, Beidi Chen, Simon Du arXiv 2023 |
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models [link] Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, Zhangyang Wang, Beidi Chen NeurIPS 2023 |
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Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer [link] [talk] [slides] Yuandong Tian, Yiping Wang, Beidi Chen, Simon Du NeurIPS 2023 |
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Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information [link] [code] Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian NeurIPS 2023 |
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RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment [link] [code] Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian arXiv 2023 |
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Extending Context Window of Large Language Models via Positional Interpolation [link] Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian arXiv 2023 |
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DOC: Improving Long Story Coherence With Detailed Outline Control [link] [code] Kevin Yang, Dan Klein, Nanyun Peng, Yuandong Tian ACL 2023 |
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time [link] Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Re, Beidi Chen ICML 2023 (Oral) |
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Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning [link] Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner ICML 2023 |
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SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems [link] [Slides] Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian ICML 2023 |
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Learning Compiler Pass Orders using Coreset and Normalized Value Prediction [link] Youwei Liang*, Kevin Stone*, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian (* = Equal 1st authors) ICML 2023 |
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models Daochen Zha, Louis Feng, Liang Luo, Bhargav Bhushanam, Zirui Liu, Yusuo Hu, Jade Nie, Yuzhen Huang, Yuandong Tian, Arun Kejariwal, Xia Hu MLSys 2023 |
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Local Branching Relaxation Heuristics for Integer Linear Programs [link] Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner CPAIOR 2023 |
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Efficient Planning in a Compact Latent Action Space [link] [code] [website] Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian ICLR 2023 |
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Understanding the Role of Nonlinearity in Training Dynamics of Contrastive Learning [link] [code] [workshop version] [workshop poster] [5min talk] Yuandong Tian ICLR 2023 |
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MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection [link] Jiaxun Cui, Xiaomeng Yang*, Geunbae Lee*, Mulong Luo*, Peter Stone, Hsien-Hsin S. Lee, Benjamin Lee, G. Edward Suh, Wenjie Xiong**, Yuandong Tian** (* = Equal 2nd authors, ** = Equal advising) ICLR 2023 |
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Modeling Scattering Coefficients using Self-Attentive Complex Polynomials with Image-based Representation [link] Andrew Cohen*, Weiping Dou, Jiang Zhu, Slawomir Koziel, Peter Renner, Jan-Ove Mattsson, Xiaomeng Yang, Beidi Chen, Kevin Stone, Yuandong Tian* (* = Equal 1st authors) arXiv 2023 |
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AutoCAT: Reinforcement Learning for Automated Exploration of Cache Timing-Channel Attacks [link] [code] Mulong Luo*, Wenjie Xiong*, Geunbae Lee, Yueying Li, Xiaomeng Yang, Amy Zhang, Yuandong Tian, Hsien Hsin S Lee, G Edward Suh (* = Equal 1st authors) HPCA 2023 |
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Re3: Generating Longer Stories With Recursive Reprompting and Revision [link] [code] Kevin Yang, Yuandong Tian, Nanyun Peng, Dan Klein EMNLP 2022 |
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Understanding Deep Contrastive Learning via Coordinate-wise Optimization [link] [code] [video] [5min talk slides] [poster] Yuandong Tian NeurIPS 2022 (Oral) |
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DreamShard: Generalizable Embedding Table Placement for Recommender Systems [link] [code] Daochen Zha, Louis Feng, Qiaoyu Tan, Zirui Liu, Kwei-Herng Lai, Bhargav Bhushanam, Yuandong Tian, Arun Kejariwal, Xia Hu NeurIPS 2022 |
AutoShard: Automated Embedding Table Sharding for Recommender Systems [link] [code] Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu KDD 2022 |
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Denoised MDPs: Learning World Models Better Than the World Itself [link] [code] [website] Tongzhou Wang, Simon S Du, Antonio Torralba, Phillip Isola, Amy Zhang, Yuandong Tian ICML 2022 |
On the Importance of Asymmetry for Siamese Representation Learning [link] [code] Xiao Wang*, Haoqi Fan*, Yuandong Tian, Daisuke Kihara, Xinlei Chen (* = Equal 1st authors) CVPR 2022 |
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Understanding Dimensional Collapse in Contrastive Self-supervised Learning [link] [code] Li Jing, Pascal Vincent, Yann LeCun, Yuandong Tian ICLR 2022 |
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NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training [link] [code] Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Vikas Chandra ICLR 2022 |
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Multi-objective Optimization by Learning Space Partitions [link] [code] Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian ICLR 2022 |
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Towards demystifying representation learning with non-contrastive self-supervision [link] Xiang Wang, Xinlei Chen, Simon S Du, Yuandong Tian arXiv 2021 |
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Sample-Efficient Neural Architecture Search by Learning Action Space [link] [code] Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian T-PAMI 2021 |
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Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and Explanations [link] [code] Hui Shi, Sicun Gao, Yuandong Tian, Xinyun Chen, Jishen Zhao AAAI 2022 |
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CompilerGym: robust, performant compiler optimization environments for AI research [link] [code] Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather CGO 2022 (Outstanding Paper) |
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NovelD: A Simple yet Effective Exploration Criterion [link] [code] [video] Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian NeurIPS 2021 |
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MADE: Exploration via Maximizing Deviation from Explored Regions [link] [code] Tianjun Zhang*, Paria Rashidinejad*, Jiantao Jiao, Yuandong Tian, Joseph Gonzalez, Stuart Russell (* = Equal 1st authors) NeurIPS 2021 |
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Learning Space Partitions for Path Planning [link] [code] Kevin Yang*, Tianjun Zhang*, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian (* = Equal 1st authors) NeurIPS 2021 |
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Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages [link] [code] Xinyun Chen, Dawn Song, Yuandong Tian NeurIPS 2021 |
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Understanding Self-supervised Learning Dynamics without Contrastive Pairs [link] [code] [video] [Slides] [Blogpost] Yuandong Tian, Xinlei Chen, Surya Ganguli ICML 2021 (Outstanding Paper Award Honorable Mention) |
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Few-shot Neural Architecture Search [link] [code] [Blogpost] Yiyang Zhao*, Linnan Wang*, Yuandong Tian, Rodrigo Fonseca, Tian Guo (* = Equal 1st authors) ICML 2021 (Long Oral) |
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Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing [link] Cheng Fu, Hanxian Huang, Xinyun Chen, Yuandong Tian, Jishen Zhao (UCSD) ICML 2021 (Long Oral) |
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Network Planning with Deep Reinforcement Learning [link] [code] Hang Zhu (JHU), Varun Gupta, Satyajeet Singh Ahuja, Yuandong Tian, Ying Zhang, Xin Jin SIGCOMM 2021 |
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FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining [link] Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, Joseph Gonzalez CVPR 2021 |
FPNAS: Fast Probabilistic Neural Architecture Search [link] Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli CVPR 2021 |
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Understanding Robustness in Teacher-Student Setting: A New Perspective [link] [Slides] Zhuolin Yang*, Zhaoxi Chen, Tiffany Cai, Xinyun Chen, Bo Li, Yuandong Tian* (* = Equal 1st authors) AIStats 2021 |
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Multi-Agent Collaboration via Reward Attribution Decomposition [link] [code] [video] [website] Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian arxiv 2020 |
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Joint Policy Search for Multi-agent Collaboration with Imperfect Information [link] [code] [video] Yuandong Tian, Qucheng Gong, Tina Jiang NeurIPS 2020 |
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Understanding Self-supervised Learning with Dual Deep Networks [link] [code] [video] Yuandong Tian, Lantao Yu, Xinlei Chen, Surya Ganguli arXiv 2020 |
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Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension [link] [code] Yuandong Tian ICML 2020 |
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Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP [link] Haonan Yu, Sergey Edunov, Yuandong Tian, Ari S. Morcos ICLR 2020 |
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Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search [link] [code] Linnan Wang, Rodrigo Fonseca, Yuandong Tian NeurIPS 2020 |
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction [link] Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu KDD 2020 |
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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions [link] [code] Alvin Wan, Xiaoliang Dai, Peizhao Zhang, Zijian He, Yuandong Tian, Saining Xie, Bichen Wu, Matthew Yu, Tao Xu, Kan Chen, Peter Vajda, Joseph Gonzalez CVPR 2020 |
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N-Bref : A High-fidelity Decompiler Exploiting Programming Structures [link] [code] [Blogpost] Cheng Fu, Kunlin Yang, Xinyun Chen, Yuandong Tian, Jishen Zhao arxiv 2020 |
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AlphaX: Exploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search [link] Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca AAAI 2020 |
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Deep Symbolic Superoptimization Without Human Knowledge [link] [code] Hui Shi, Yang Zhang, Xinyun Chen, Yuandong Tian, Jishen Zhao ICLR 2020 |
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Hierarchical Decision Making by Generating and Following Natural Language Instructions [link] [code] Hengyuan Hu*, Denis Yarats*, Qucheng Gong, Yuandong Tian, Mike Lewis (* = Equal 1st authors) NeurIPS 2019 |
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ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero [link] [code] [pretrained model and game records] [Other resources] [Blogpost] [Talk] An open source reimplementation of DeepMind's zero-knowledge training and its application to the game of Go. Trained on 2000 GPUs for 9 days. With a single GPU and 50 seconds per move, the model won 20-0 versus 4 top 30 professional players, given human unlimited thinking time. It also won 980-18 versus LeelaZero (version Apr. 25). Yuandong Tian, Jerry Ma*, Qucheng Gong*, Shubho Sengupta*, Zhuoyuan Chen, James Pinkerton, Larry Zitnick (* = Equal 2nd authors) ICML 2019 (Long Oral) |
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Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees [link] [code] Yuping Luo*, Huazhe Xu*, Yuanzhi Li, Yuandong Tian, Trevor Darrell, Tengyu Ma (* = Equal 1st authors) ICLR 2019 |
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M^3RL: Mind-aware Multi-agent Management Reinforcement Learning [link] [code] Tianmin Shu, Yuandong Tian ICLR 2019 |
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Luck Matters: Understanding Training Dynamics of Deep ReLU Networks [link] [code] [Poster] Yuandong Tian, Tina Jiang, Qucheng Gong, Ari Morcos ICML-workshop 2019 |
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers [link] Ari S. Morcos, Haonan Yu, Michela Paganini, Yuandong Tian NeurIPS 2019 |
Real-world video adaptation with reinforcement learning [link] Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy ICML-Workshop 2019 |
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FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search [link] [code] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer CVPR 2019 |
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Learning to Perform Local Rewriting for Combinatorial Optimization [link] [code] Xinyun Chen, Yuandong Tian NeurIPS 2019 |
Coda: An End-to-End Neural Program Decompiler [link] Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao NeurIPS 2019 |
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Building Generalizable Agents with a Realistic and Rich 3D Environment [link] [code] Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian ICLR-Workshop 2018 |
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A Theoretical Framework for Deep Locally Connected ReLU Network [link] [Poster] Yuandong Tian arxiv 2018 |
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Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima [link] Simon S. Du, Jason D. Lee, Yuandong Tian, Barnabas Poczos, Aarti Singh ICML 2018 (Long Oral) |
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When is a Convolutional Filter Easy To Learn? [link] Simon S. Du, Jason D. Lee, Yuandong Tian ICLR 2018 |
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ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games [link] [code] [video] Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, Larry Zitnick NeurIPS 2017 (Oral) |
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Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning [link] Yuxin Wu, Yuandong Tian ICLR 2017 |
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An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis [link] [code] Yuandong Tian ICML 2017 |
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Semantic Amodal Segmentation [link] Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollár CVPR 2017 |
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Better Computer Go Player with Neural Network and Long-term Prediction [link] [code] [pretrained model] [mit tech review] Yuandong Tian, Yan Zhu ICLR 2016 |
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Single Image 3D Interpreter Network [link] Jiajun Wu*, Tianfan Xue*, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman (* = Equal 1st authors) ECCV 2016 (Oral) |
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Simple Baseline for Visual Question Answering [link] [code] Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus arxiv 2016 |
Theory and Practice of Hierarchical Data-driven Descent for Optimal Deformation Estimation [link] Yuandong Tian, Srinivasa G. Narasimhan IJCV 2015 |
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Theory and Practice of Globally Optimal Deformation Estimation [link] Yuandong Tian PhD thesis 2013 |
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Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation [link] [Proofs] Yuandong Tian, Srinivasa G. Narasimhan ICCV 2013 (Marr Prize Honorable Mentions) |
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Integrating Perceptual Learning with External World Knowledge in a Simulated Student [link] Nan Li, Yuandong Tian, William W. Cohen, Ken Koedinger AIED 2013 |
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Exploring the Spatial Hierarchy of Mixture Models for Human Pose Estimation [link] [code] [website] Yuandong Tian, Larry Zitnick, Srinivasa G. Narasimhan ECCV 2012 |
Learning from Crowds in the Presence of Schools of Thought [link] [code] [Slides] [Dataset] Yuandong Tian, Jun Zhu KDD 2012 |
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Depth from Optical Turbulence [link] [website] Yuandong Tian, Srinivasa G. Narasimhan, Alan J. Vannevel CVPR 2012 |
A Combined Theory of Defocused Illumination and Global Light Transport [link] Mohit Gupta, Yuandong Tian, Srinivasa G. Narasimhan, Li Zhang IJCV 2011 |
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Globally Optimal Estimation of Nonrigid Image Distortion [link] Yuandong Tian, Srinivasa G. Narasimhan IJCV 2011 |
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Rectification and 3D reconstruction of Curved Document Images [link] [website] Yuandong Tian, Srinivasa G. Narasimhan CVPR 2011 (Oral) |
Local Isomorphism to Solve the Pre-image Problem in Kernel Methods [link] Dong Huang, Yuandong Tian, Fernando De la Torre CVPR 2011 |
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A Globally Optimal Data-Driven Approach for Image Distortion Estimation [link] [website] Yuandong Tian, Srinivasa G. Narasimhan CVPR 2010 (Oral) |
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Seeing through water: Image restoration using model-based tracking [link] [website] Yuandong Tian, Srinivasa G. Narasimhan ICCV 2009 |
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(De) Focusing on Global Light Transport for Active Scene Recovery [link] Mohit Gupta, Yuandong Tian, Srinivasa G. Narasimhan, Li Zhang CVPR 2009 (Oral) |