Neural network-based clustering using pairwise constraints. - Yen-Chang Hsu; Zsolt Kira (CoRR, 2015)

Deep Image Category Discovery using a Transferred Similarity Function. - Yen-Chang Hsu; Zhaoyang Lv; Zsolt Kira (CoRR, 2016)

Learning Loss for Knowledge Distillation with Conditional Adversarial Networks. - Zheng Xu 002; Yen-Chang Hsu; Jiawei Huang 006 (CoRR, 2017)

Training Student Networks for Acceleration with Conditional Adversarial Networks. - Zheng Xu 002; Yen-Chang Hsu; Jiawei Huang 006 (BMVC, 2018)

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks. - Zheng Xu 002; Yen-Chang Hsu; Jiawei Huang 006 (ICLR (Workshop), 2018)

Learning to cluster in order to transfer across domains and tasks. - Yen-Chang Hsu; Zhaoyang Lv; Zsolt Kira (ICLR (Poster), 2018)

Learning to Cluster for Proposal-Free Instance Segmentation. - Yen-Chang Hsu; Zheng Xu 002; Zsolt Kira; Jiawei Huang 006 (IJCNN, 2018)

A probabilistic constrained clustering for transfer learning and image category discovery. - Yen-Chang Hsu; Zhaoyang Lv; Joel Schlosser; Phillip Odom; Zsolt Kira (CoRR, 2018)

Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines. - Yen-Chang Hsu; Yen-Cheng Liu; Zsolt Kira (CoRR, 2018)

Multi-class classification without multi-class labels. - Yen-Chang Hsu; Zhaoyang Lv; Joel Schlosser; Phillip Odom; Zsolt Kira (ICLR (Poster), 2019)

Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data. - Yen-Chang Hsu; Yilin Shen; Hongxia Jin; Zsolt Kira (CVPR, 2020)

Posterior Re-calibration for Imbalanced Datasets. - Junjiao Tian; Yen-Cheng Liu; Nathaniel Glaser; Yen-Chang Hsu; Zsolt Kira (NeurIPS, 2020)

Learning from pairwise similarity for visual categorization. - Yen-Chang Hsu (2020)

Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU. - Yilin Shen; Yen-Chang Hsu; Avik Ray; Hongxia Jin (ACL/IJCNLP (1), 2021)

Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning. - James Seale Smith; Yen-Chang Hsu; Jonathan Balloch; Yilin Shen; Hongxia Jin; Zsolt Kira (ICCV, 2021)

Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer. - James Seale Smith; Jonathan Balloch; Yen-Chang Hsu; Zsolt Kira (IJCNN, 2021)

Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding. - Ting Hua; Yilin Shen; Changsheng Zhao 002; Yen-Chang Hsu; Hongxia Jin (NAACL-HLT, 2021)

A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition. - Junjiao Tian; Dylan Yung; Yen-Chang Hsu; Zsolt Kira (NeurIPS, 2021)

Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data. - Junjiao Tian; Yen-Chang Hsu; Yilin Shen; Hongxia Jin; Zsolt Kira (CoRR, 2021)

Lite-MDETR: A Lightweight Multi-Modal Detector. - Qian Lou; Yen-Chang Hsu; Burak Uzkent; Ting Hua; Yilin Shen; Hongxia Jin (CVPR, 2022)

Numerical Optimizations for Weighted Low-rank Estimation on Language Models. - Ting Hua; Yen-Chang Hsu; Felicity Wang; Qian Lou; Yilin Shen; Hongxia Jin (EMNLP, 2022)

Language model compression with weighted low-rank factorization. - Yen-Chang Hsu; Ting Hua; Sungen Chang; Qian Lou; Yilin Shen; Hongxia Jin (ICLR, 2022)

DictFormer: Tiny Transformer with Shared Dictionary. - Qian Lou; Ting Hua; Yen-Chang Hsu; Yilin Shen; Hongxia Jin (ICLR, 2022)

A Closer Look at Knowledge Distillation with Features, Logits, and Gradients. - Yen-Chang Hsu; James Seale Smith; Yilin Shen; Zsolt Kira; Hongxia Jin (CoRR, 2022)

A Closer Look at Rehearsal-Free Continual Learning. - James Seale Smith; Junjiao Tian; Yen-Chang Hsu; Zsolt Kira (CoRR, 2022)

Numerical Optimizations for Weighted Low-rank Estimation on Language Model. - Ting Hua; Yen-Chang Hsu; Felicity Wang; Qian Lou; Yilin Shen; Hongxia Jin (CoRR, 2022)

A Closer Look at Rehearsal-Free Continual Learning. - James Seale Smith; Junjiao Tian; Shaunak Halbe; Yen-Chang Hsu; Zsolt Kira (CVPR Workshops, 2023)

Dynamic Low-rank Estimation for Transformer-based Language Models. - Ting Hua; Xiao Li; Shangqian Gao; Yen-Chang Hsu; Yilin Shen; Hongxia Jin (EMNLP (Findings), 2023)

Training Energy-Based Normalizing Flow with Score-Matching Objectives. - Chen-Hao Chao; Wei-Fang Sun; Yen-Chang Hsu; Zsolt Kira; Chun-Yi Lee (NeurIPS, 2023)

Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters. - James Seale Smith; Yen-Chang Hsu; Zsolt Kira; Yilin Shen; Hongxia Jin (CVPR Workshops, 2024)

Adaptive Rank Selections for Low-Rank Approximation of Language Models. - Shangqian Gao; Ting Hua; Yen-Chang Hsu; Yilin Shen; Hongxia Jin (NAACL-HLT, 2024)

SLiM: Speculative Decoding with Hypothesis Reduction. - Chi-Heng Lin; Shikhar Tuli; James Seale Smith; Yen-Chang Hsu; Yilin Shen; Hongxia Jin (NAACL-HLT (Findings), 2024)

DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling. - Shikhar Tuli; Chi-Heng Lin; Yen-Chang Hsu; Niraj K. Jha; Yilin Shen; Hongxia Jin (NAACL-HLT, 2024)

DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models. - Shangqian Gao; Chi-Heng Lin; Ting Hua; Zheng Tang; Yilin Shen; Hongxia Jin; Yen-Chang Hsu (NeurIPS, 2024)

Token Fusion: Bridging the Gap between Token Pruning and Token Merging. - Minchul Kim; Shangqian Gao; Yen-Chang Hsu; Yilin Shen; Hongxia Jin (WACV, 2024)

Retraining-Free Merging of Sparse Mixture-of-Experts via Hierarchical Clustering. - I-Chun Chen; Hsu-Shen Liu; Wei-Fang Sun; Chen-Hao Chao; Yen-Chang Hsu; Chun-Yi Lee (CoRR, 2024)

DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models. - Shangqian Gao; Chi-Heng Lin; Ting Hua; Tang Zheng; Yilin Shen; Hongxia Jin; Yen-Chang Hsu (CoRR, 2024)

Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA. - James Seale Smith; Yen-Chang Hsu; Lingyu Zhang; Ting Hua; Zsolt Kira; Yilin Shen; Hongxia Jin (Trans. Mach. Learn. Res., 2024)

MoDeGPT: Modular Decomposition for Large Language Model Compression. - Chi-Heng Lin; Shangqian Gao; James Seale Smith; Abhishek Patel; Shikhar Tuli; Yilin Shen; Hongxia Jin; Yen-Chang Hsu (ICLR, 2025)

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing. - James Seale Smith; Chi-Heng Lin; Shikhar Tuli; Haris Jeelani; Shangqian Gao; Yilin Shen; Hongxia Jin; Yen-Chang Hsu (NAACL (Long Papers), 2025)

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning. - Shangqian Gao; Ting Hua; Reza Shirkavand; Chi-Heng Lin; Zhen Tang; Zhengao Li; Longge Yuan; Fangyi Li; Zeyu Zhang; Alireza Ganjdanesh; Qian Lou; Xu Jie; et al. (CoRR, 2025)