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Delving into Deep Imbalanced Regression


Feb 18, 2021
Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Hao Wang, Dina Katabi

* Code and data are available at https://github.com/YyzHarry/imbalanced-regression 

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Information-Preserving Contrastive Learning for Self-Supervised Representations


Dec 17, 2020
Tianhong Li, Lijie Fan, Yuan Yuan, Hao He, Yonglong Tian, Dina Katabi

* The first two authors contributed equally to this paper 

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In-Home Daily-Life Captioning Using Radio Signals


Aug 25, 2020
Lijie Fan, Tianhong Li, Yuan Yuan, Dina Katabi

* ECCV 2020. The first two authors contributed equally to this paper 

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Continuously Indexed Domain Adaptation


Jul 03, 2020
Hao Wang, Hao He, Dina Katabi

* Accepted at ICML 2020 

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Learning Longterm Representations for Person Re-Identification Using Radio Signals


Apr 02, 2020
Lijie Fan, Tianhong Li, Rongyao Fang, Rumen Hristov, Yuan Yuan, Dina Katabi

* CVPR 2020. The first three authors contributed equally to this paper 

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Learning Compositional Koopman Operators for Model-Based Control


Oct 18, 2019
Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba

* The first two authors contributed equally to this paper. Project Page: http://koopman.csail.mit.edu/ Video: https://www.youtube.com/watch?v=idFH4K16cfQ&feature=youtu.be 

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Harnessing Structures for Value-Based Planning and Reinforcement Learning


Sep 26, 2019
Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi


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Making the Invisible Visible: Action Recognition Through Walls and Occlusions


Sep 20, 2019
Tianhong Li, Lijie Fan, Mingmin Zhao, Yingcheng Liu, Dina Katabi

* ICCV 2019. The first two authors contributed equally to this paper 

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ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation


May 28, 2019
Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu

* ICML 2019 

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Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling


Feb 06, 2019
Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, Dina Katabi

* Appeared at AAAI 2019 

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