| Delving into Deep Imbalanced Regression Y Yang, K Zha, YC Chen, H Wang, D Katabi International Conference on Machine Learning (ICML), 2021, 2021 | 244 | 2021 |
| Deep RNN Framework for Visual Sequential Applications B Pang*, K Zha*, H Cao, C Shi, C Lu Computer Vision and Pattern Recognition (CVPR), 2019, 2019 | 51 | 2019 |
| Further Understanding Videos through Adverbs: A New Video Task B Pang, K Zha, Y Zhang, C Lu AAAI Conference on Artificial Intelligence (AAAI), 2020, 2020 | 29* | 2020 |
| Rank-N-Contrast: Learning Continuous Representations for Regression K Zha*, P Cao*, J Son, Y Yang, D Katabi Neural Information Processing Systems (NeurIPS), 2023, 2023 | 23* | 2023 |
| Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts B Pang, K Zha, H Cao, J Tang, M Yu, C Lu Nature Machine Intelligence, 2020 | 22 | 2020 |
| Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning H He*, K Zha*, D Katabi International Conference on Learning Representations (ICLR), 2023, 2022 | 19 | 2022 |
| Cornerradar: Rf-based indoor localization around corners S Yue, H He, P Cao, K Zha, M Koizumi, D Katabi Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/Ubicomp …, 2022 | 16 | 2022 |
| Unsupervised Representation for Semantic Segmentation by Implicit Cycle-Attention Contrastive Learning B Pang, Y Li, Y Zhang, G Peng, J Tang, K Zha, J Li, C Lu AAAI Conference on Artificial Intelligence (AAAI), 2022, 2022 | 8 | 2022 |
| Unsupervised Image Transformation Learning via Generative Adversarial Networks K Zha, Y Shen, B Zhou arXiv preprint arXiv:2103.07751, 2021 | 1 | 2021 |
| Deep Imbalanced Regression: Challenges, Methods, and Applications K Zha Massachusetts Institute of Technology, 2022 | | 2022 |
Dina KatabiThuan and Nicole Pham Professor, MITVerified email at ai.mit.edu
