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2020 – today
- 2024
[i12]Zichang Liu, Qingyun Liu, Yuening Li, Liang Liu, Anshumali Shrivastava, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao:
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model. CoRR abs/2402.14035 (2024)- 2023
- [j1]Siyu Li, Zichang Liu
, Yunbin Yan, Rongcai Wang, Enzhi Dong, Zhonghua Cheng:
Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer. Sensors 23(14): 6447 (2023) - [c13]Zichang Liu, Hui Liang, Siyu Li, Rongcai Wang, Yueming Han:
Golden Sine SABO Integrating Multiple Strategies. ICAICE 2023: 36-41 - [c12]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. ICLR 2023 - [c11]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. ICML 2023: 22137-22176 - [c10]Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, Anshumali Shrivastava:
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time. NeurIPS 2023 - [c9]Zichang Liu, Zhaozhuo Xu, Benjamin Coleman, Anshumali Shrivastava:
One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. NeurIPS 2023 - [i11]Zichang Liu, Aditya Desai, Fangshuo Liao, Weitao Wang, Victor Xie, Zhaozhuo Xu, Anastasios Kyrillidis, Anshumali Shrivastava:
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time. CoRR abs/2305.17118 (2023) - [i10]Zichang Liu, Jue Wang, Tri Dao, Tianyi Zhou, Binhang Yuan, Zhao Song, Anshumali Shrivastava, Ce Zhang, Yuandong Tian, Christopher Ré, Beidi Chen:
Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time. CoRR abs/2310.17157 (2023) - [i9]Aditya Desai, Benjamin Meisburger, Zichang Liu, Anshumali Shrivastava:
Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces. CoRR abs/2311.01722 (2023) - 2022
- [c8]Haitao Lin, Zichang Liu, Chilam Cheang, Yanwei Fu, Guodong Guo, Xiangyang Xue:
SAR-Net: Shape Alignment and Recovery Network for Category-level 6D Object Pose and Size Estimation. CVPR 2022: 6697-6707 - [c7]Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Junyan Zhang, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
HALOS: Hashing Large Output Space for Cheap Inference. MLSys 2022 - [c6]Zichang Liu, Benjamin Coleman, Tianyi Zhang, Anshumali Shrivastava:
Retaining Knowledge for Learning with Dynamic Definition. NeurIPS 2022 - [i8]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. CoRR abs/2212.14453 (2022) - 2021
- [c5]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. DATE 2021: 1426-1429 - [c4]Beidi Chen, Zichang Liu, Binghui Peng, Zhaozhuo Xu, Jonathan Lingjie Li, Tri Dao, Zhao Song, Anshumali Shrivastava, Christopher Ré:
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training. ICLR 2021 - [c3]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. ECML/PKDD (1) 2021: 772-787 - [i7]Li Chou, Zichang Liu, Zhuang Wang, Anshumali Shrivastava:
Efficient and Less Centralized Federated Learning. CoRR abs/2106.06627 (2021) - [i6]Zichang Liu, Benjamin Coleman, Anshumali Shrivastava:
Efficient Inference via Universal LSH Kernel. CoRR abs/2106.11426 (2021) - [i5]Haitao Lin, Zichang Liu, Chilam Cheang, Lingwei Zhang, Yanwei Fu, Xiangyang Xue:
DONet: Learning Category-Level 6D Object Pose and Size Estimation from Depth Observation. CoRR abs/2106.14193 (2021) - 2020
- [i4]Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
Climbing the WOL: Training for Cheaper Inference. CoRR abs/2007.01230 (2020) - [i3]Yixin Liu, Yong Guo, Zichang Liu, Haohua Liu, Jingjie Zhang, Zejun Chen, Jing Liu, Jian Chen:
Conditional Automated Channel Pruning for Deep Neural Networks. CoRR abs/2009.09724 (2020) - [i2]Zichang Liu, Li Chou, Anshumali Shrivastava:
Neighbor Oblivious Learning (NObLe) for Device Localization and Tracking. CoRR abs/2011.14954 (2020)
2010 – 2019
- 2019
- [c2]Carlos Bernal-Cárdenas, Kevin Moran, Michele Tufano, Zichang Liu, Linyong Nan, Zhehan Shi, Denys Poshyvanyk:
Guigle: a GUI search engine for Android apps. ICSE (Companion Volume) 2019: 71-74 - [i1]Carlos Bernal-Cárdenas, Kevin Moran, Michele Tufano, Zichang Liu, Linyong Nan, Zhehan Shi, Denys Poshyvanyk:
Guigle: A GUI Search Engine for Android Apps. CoRR abs/1901.00891 (2019) - 2018
- [c1]Zhen Du, Lipeng Ma, Huakang Li, Qun Li, Guozi Sun, Zichang Liu:
Network Traffic Anomaly Detection Based on Wavelet Analysis. SERA 2018: 94-101
Coauthor Index
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