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ORCIDCheng-Yu Hsieh
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2020 – today
- 2024
[c16]Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization. ACL (Findings) 2024: 14982-14995- [c15]Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, Ranjay Krishna, Yoon Kim, James R. Glass:
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps. EMNLP 2024: 1419-1436 - [c14]Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu:
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning. EMNLP 2024: 18089-18099 - [c13]Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Kumar Jaiswal, Mykola Pechenizkiy, Yi Liang, Michael Bendersky, Zhangyang Wang, Shiwei Liu:
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. ICML 2024 - [i17]Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan, Matthew Wallingford, Chun-Liang Li, Pang Wei Koh, Ranjay Krishna:
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better. CoRR abs/2406.05184 (2024) - [i16]Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar:
DataComp-LM: In search of the next generation of training sets for language models. CoRR abs/2406.11794 (2024) - [i15]Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization. CoRR abs/2406.16008 (2024) - [i14]Yu-Guan Hsieh, Cheng-Yu Hsieh, Shih-Ying Yeh, Louis Béthune, Hadipour Ansari, Pavan Kumar Anasosalu Vasu, Chun-Liang Li, Ranjay Krishna, Oncel Tuzel, Marco Cuturi:
Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions. CoRR abs/2407.06723 (2024) - [i13]Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, Ranjay Krishna, Yoon Kim, James R. Glass:
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps. CoRR abs/2407.07071 (2024) - [i12]Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Kumar Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu:
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning. CoRR abs/2410.07461 (2024) - 2023
- [c12]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. ACL (Findings) 2023: 8003-8017 - [c11]Cheng-Yu Hsieh, Jieyu Zhang, Zixian Ma, Aniruddha Kembhavi, Ranjay Krishna:
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality. NeurIPS 2023 - [i11]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. CoRR abs/2305.02301 (2023) - [i10]Cheng-Yu Hsieh, Jieyu Zhang, Zixian Ma, Aniruddha Kembhavi, Ranjay Krishna:
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality. CoRR abs/2306.14610 (2023) - [i9]Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister:
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models. CoRR abs/2308.00675 (2023) - [i8]Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu:
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. CoRR abs/2310.05175 (2023) - 2022
- [j7]Ching-Chi Lin, Cheng-Yu Hsieh, Ta-Yu Mu:
A linear-time algorithm for weighted paired-domination on block graphs. J. Comb. Optim. 44(1): 269-286 (2022) - [j6]Cheng-Yu Hsieh, Jieyu Zhang, Alexander J. Ratner:
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. Proc. VLDB Endow. 15(13): 4093-4105 (2022) - [c10]Cheng-Yu Hsieh, Rung-Tzuo Liaw:
Summit-assisted Evolutionary Multitasking. CEC 2022: 1-8 - [c9]Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. NeurIPS 2022 - [i7]Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, Alexander Ratner:
A Survey on Programmatic Weak Supervision. CoRR abs/2202.05433 (2022) - [i6]Cheng-Yu Hsieh, Jieyu Zhang, Alexander Ratner:
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming. CoRR abs/2203.01382 (2022) - [i5]Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander Ratner:
Understanding Programmatic Weak Supervision via Source-aware Influence Function. CoRR abs/2205.12879 (2022) - 2021
- [c8]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [i4]Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama:
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach. CoRR abs/2109.14676 (2021) - 2020
- [c7]Naai-Jung Shih, Cheng-Yu Hsieh, Yi Chen, Pei-Huang Diao:
Computing Urban Fabric of a Historical Town. CATA 2020: 80-89 - [i3]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020)
2010 – 2019
- 2019
- [c6]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
On the (In)fidelity and Sensitivity of Explanations. NeurIPS 2019: 10965-10976 - [i2]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
How Sensitive are Sensitivity-Based Explanations? CoRR abs/1901.09392 (2019) - 2018
- [j5]Chih-Kuan Yeh, Cheng-Yu Hsieh
, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. IEEE Trans. Games 10(4): 365-377 (2018) - [c5]Cheng-Yu Hsieh, Yi-An Lin, Hsuan-Tien Lin:
A Deep Model With Local Surrogate Loss for General Cost-Sensitive Multi-Label Learning. AAAI 2018: 3239-3246 - 2016
- [i1]Ching-Chi Lin, Cheng-Yu Hsieh:
A Linear-Time Algorithm for the Weighted Paired-Domination Problem on Block Graphs. CoRR abs/1605.00372 (2016) - 2013
- [j4]Chih-Yi Chiu, Tsung-Han Tsai, Cheng-Yu Hsieh:
Efficient video segment matching for detecting temporal-based video copies. Neurocomputing 105: 70-80 (2013) - [j3]Chih-Yi Chiu, Tsung-Han Tsai, Guei-Wun Han, Cheng-Yu Hsieh, Sheng-Yang Li:
Efficient Video Stream Monitoring for Near-Duplicate Detection and Localization in a Large-Scale Repository. ACM Trans. Inf. Syst. 31(4): 22:1-22:27 (2013) - [c4]Yi-Chia Lee, Chia-Yu Yao, Cheng-Yu Hsieh, Jau-Yi Wu, Yi-Hsuan Hsieh, Chien-Hsiung Chen, Rung-Huei Liang, Ya-Shu Chen:
Egg Pair - A hearing game for the visually impaired people using RFID. ISCE 2013: 3-4 - 2012
- [j2]Chih-Yi Chiu, Sheng-Yang Li, Cheng-Yu Hsieh:
Video Query Reformulation for Near-Duplicate Detection. IEEE Trans. Inf. Forensics Secur. 7(5): 1594-1603 (2012) - [c3]Chih-Yi Chiu, Tsung-Han Tsai, Cheng-Yu Hsieh:
Scalable near-duplicate video stream monitoring. ISPACS 2012: 12-15 - 2011
- [c2]Sok-Ian Sou, Cheng-Yu Hsieh, Fen-Yen Lee, Yu-Fu Lin, Jeu-Yih Jeng, Chien-Wei Cheng:
Design and implementation of dynamic charging plan for IMS-based multicast services. APNOMS 2011: 1-4
2000 – 2009
- 2007
- [j1]Cheng-Yu Hsieh, Wanjiun Liao:
All-optical multicast routing in sparse splitting WDM networks. IEEE J. Sel. Areas Commun. 25(S-6): 51-62 (2007) - 2003
- [c1]Cheng-Yu Hsieh, Wanjiun Liao:
All Optical Multicast Routing in Sparse-Splitting Optical Networks. LCN 2003: 162-167
Coauthor Index
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