Leonard Lausen

According to our database, Leonard Lausen authored at least 13 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning.

Soumajyoti Sarkar, Leonard Lausen, Volkan Cevher, Sheng Zha, Thomas Brox, George Karypis

CoRR, 2024

2023

Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks.

Soumajyoti Sarkar, Leonard Lausen

CoRR, 2023

Large Language Models of Code Fail at Completing Code with Potential Bugs.

Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

HyTrel: Hypergraph-enhanced Tabular Data Representation Learning.

Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, George Karypis

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion.

Hengzhi Pei, Jinman Zhao, Leonard Lausen, Sheng Zha, George Karypis

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic.

Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta, Leonard Lausen, Sheng Zha, Saab Mansour

CoRR, 2022

Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning.

Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

2020

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

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

J. Mach. Learn. Res., 2020

2019

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

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

CoRR, 2019

Dive into Deep Learning for Natural Language Processing.

Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

2017

NSML: A Machine Learning Platform That Enables You to Focus on Your Models.

Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Dong-Hyun Kwak, Jung-Woo Ha, Sunghun Kim

CoRR, 2017

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model.

Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, Wang-chun Woo

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

CrowdRisk: exploring crowdsourcing of risk information.

Leonard Lausen, Markus Rittenbruch, Peta Mitchell, Ella Horton, Marcus Foth

Proceedings of the 28th Australian Conference on Computer-Human Interaction, 2016