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2020 – today
- 2024
[i10]Soumajyoti Sarkar, Leonard Lausen, Volkan Cevher, Sheng Zha, Thomas Brox, George Karypis:
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning. CoRR abs/2409.01483 (2024)- 2023
- [c7]Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis:
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion. AAAI 2023: 5230-5238 - [c6]Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis:
HyTrel: Hypergraph-enhanced Tabular Data Representation Learning. NeurIPS 2023 - [c5]Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis:
Large Language Models of Code Fail at Completing Code with Potential Bugs. NeurIPS 2023 - [i9]Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis:
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion. CoRR abs/2306.00381 (2023) - [i8]Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis:
Large Language Models of Code Fail at Completing Code with Potential Bugs. CoRR abs/2306.03438 (2023) - [i7]Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis:
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning. CoRR abs/2307.08623 (2023) - [i6]Soumajyoti Sarkar, Leonard Lausen:
Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks. CoRR abs/2310.00789 (2023) - 2022
- [c4]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. NAACL-HLT 2022: 2542-2550 - [i5]Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis:
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning. CoRR abs/2204.11117 (2022) - [i4]Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta, Leonard Lausen, Sheng Zha, Saab Mansour:
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic. CoRR abs/2211.03966 (2022) - 2020
- [j1]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng, Yi Zhu:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. J. Mach. Learn. Res. 21: 23:1-23:7 (2020)
2010 – 2019
- 2019
- [c3]Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola:
Dive into Deep Learning for Natural Language Processing. EMNLP/IJCNLP (2) 2019 - [i3]Jian Guo, He He, Tong He, Leonard Lausen, Mu Li, Haibin Lin, Xingjian Shi, Chenguang Wang, Junyuan Xie, Sheng Zha, Aston Zhang, Hang Zhang, Zhi Zhang, Zhongyue Zhang, Shuai Zheng
:
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing. CoRR abs/1907.04433 (2019) - 2017
- [c2]Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. NIPS 2017: 5617-5627 - [i2]Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo:
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. CoRR abs/1706.03458 (2017) - [i1]Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Dong-Hyun Kwak, Jung-Woo Ha, Sunghun Kim:
NSML: A Machine Learning Platform That Enables You to Focus on Your Models. CoRR abs/1712.05902 (2017) - 2016
- [c1]Leonard Lausen, Markus Rittenbruch, Peta Mitchell, Ella Horton, Marcus Foth:
CrowdRisk: exploring crowdsourcing of risk information. OZCHI 2016: 165-169
Coauthor Index
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