Yen-Chang Hsu

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 48

Top Topics

Imbalanced Datasets

Incremental Learning

Pairwise Constraints

Top Venues

Publications

Shangqian Gao, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
Adaptive Rank Selections for Low-Rank Approximation of Language Models. NAACL-HLT (2024)
Minchul Kim, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
Token Fusion: Bridging the Gap between Token Pruning and Token Merging. WACV (2024)
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu, Niraj K. Jha, Yilin Shen, Hongxia Jin
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling. CoRR (2024)
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA. Trans. Mach. Learn. Res. 2024 (2024)
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu, Niraj K. Jha, Yilin Shen, Hongxia Jin
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling. NAACL-HLT (2024)
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA. CoRR (2023)
James Seale Smith, Junjiao Tian, Shaunak Halbe, Yen-Chang Hsu, Zsolt Kira
A Closer Look at Rehearsal-Free Continual Learning. CVPR Workshops (2023)
Chen-Hao Chao, Wei-Fang Sun, Yen-Chang Hsu, Zsolt Kira, Chun-Yi Lee
Training Energy-Based Normalizing Flow with Score-Matching Objectives. CoRR (2023)
Chen-Hao Chao, Wei-Fang Sun, Yen-Chang Hsu, Zsolt Kira, Chun-Yi Lee
Training Energy-Based Normalizing Flow with Score-Matching Objectives. NeurIPS (2023)
Minchul Kim, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
Token Fusion: Bridging the Gap between Token Pruning and Token Merging. CoRR (2023)
Ting Hua, Xiao Li, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
Dynamic Low-rank Estimation for Transformer-based Language Models. EMNLP (Findings) (2023)
James Seale Smith, Yen-Chang Hsu, Zsolt Kira, Yilin Shen, Hongxia Jin
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters. CoRR (2023)
Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin
Numerical Optimizations for Weighted Low-rank Estimation on Language Model. CoRR (2022)
James Seale Smith, Junjiao Tian, Yen-Chang Hsu, Zsolt Kira
A Closer Look at Rehearsal-Free Continual Learning. CoRR (2022)
Qian Lou, Yen-Chang Hsu, Burak Uzkent, Ting Hua, Yilin Shen, Hongxia Jin
Lite-MDETR: A Lightweight Multi-Modal Detector. CVPR (2022)
Yen-Chang Hsu, Ting Hua, Sungen Chang, Qian Lou, Yilin Shen, Hongxia Jin
Language model compression with weighted low-rank factorization. CoRR (2022)
Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin
Numerical Optimizations for Weighted Low-rank Estimation on Language Models. EMNLP (2022)
Ting Hua, Yilin Shen, Changsheng Zhao, Yen-Chang Hsu, Hongxia Jin
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding. CoRR (2022)
Yen-Chang Hsu, Ting Hua, Sungen Chang, Qian Lou, Yilin Shen, Hongxia Jin
Language model compression with weighted low-rank factorization. ICLR (2022)
Qian Lou, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
DictFormer: Tiny Transformer with Shared Dictionary. ICLR (2022)
Yen-Chang Hsu, James Smith, Yilin Shen, Zsolt Kira, Hongxia Jin
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients. CoRR (2022)
Junjiao Tian, Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data. CoRR (2021)
James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, Zsolt Kira
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning. ICCV (2021)
Ting Hua, Yilin Shen, Changsheng Zhao, Yen-Chang Hsu, Hongxia Jin
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding. NAACL-HLT (2021)
Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition. CoRR (2021)
James Smith, Jonathan Balloch, Yen-Chang Hsu, Zsolt Kira
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer. IJCNN (2021)
James Smith, Jonathan Balloch, Yen-Chang Hsu, Zsolt Kira
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer. CoRR (2021)
James Smith, Yen-Chang Hsu, Jonathan Balloch, Yilin Shen, Hongxia Jin, Zsolt Kira
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning. CoRR (2021)
Yilin Shen, Yen-Chang Hsu, Avik Ray, Hongxia Jin
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU. CoRR (2021)
Yilin Shen, Yen-Chang Hsu, Avik Ray, Hongxia Jin
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU. ACL/IJCNLP (1) (2021)
Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition. NeurIPS (2021)
Junjiao Tian, Yen-Cheng Liu, Nathan Glaser, Yen-Chang Hsu, Zsolt Kira
Posterior Re-calibration for Imbalanced Datasets. CoRR (2020)
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data. CVPR (2020)
Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira
Posterior Re-calibration for Imbalanced Datasets. NeurIPS (2020)
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data. CoRR (2020)
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
Multi-class Classification without Multi-class Labels. CoRR (2019)
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
Multi-class classification without multi-class labels. ICLR (Poster) (2019)
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Learning to cluster in order to transfer across domains and tasks. ICLR (Poster) (2018)
Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
Learning to Cluster for Proposal-Free Instance Segmentation. IJCNN (2018)
Yen-Chang Hsu, Yen-Cheng Liu, Zsolt Kira
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines. CoRR (2018)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. ICLR (Workshop) (2018)
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira
A probabilistic constrained clustering for transfer learning and image category discovery. CoRR (2018)
Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang
Learning to Cluster for Proposal-Free Instance Segmentation. CoRR (2018)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Training Student Networks for Acceleration with Conditional Adversarial Networks. BMVC (2018)
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Learning to cluster in order to Transfer across domains and tasks. CoRR (2017)
Zheng Xu, Yen-Chang Hsu, Jiawei Huang
Learning Loss for Knowledge Distillation with Conditional Adversarial Networks. CoRR (2017)
Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira
Deep Image Category Discovery using a Transferred Similarity Function. CoRR (2016)
Yen-Chang Hsu, Zsolt Kira
Neural network-based clustering using pairwise constraints. CoRR (2015)