Yen-Chang Hsu

According to our database, Yen-Chang Hsu authored at least 38 papers between 2015 and 2024.

Collaborative distances:

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

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

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

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

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

CoRR, 2024

Token Fusion: Bridging the Gap between Token Pruning and Token Merging.

Minchul Kim, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 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

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

SLiM: Speculative Decoding with Hypothesis Reduction.

Chi-Heng Lin, Shikhar Tuli, James Seale Smith, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

Adaptive Rank Selections for Low-Rank Approximation of Language Models.

Shangqian Gao, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters.

James Seale Smith, Yen-Chang Hsu, Zsolt Kira, Yilin Shen, Hongxia Jin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Training Energy-Based Normalizing Flow with Score-Matching Objectives.

Chen-Hao Chao, Wei-Fang Sun, Yen-Chang Hsu, Zsolt Kira, Chun-Yi Lee

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

Dynamic Low-rank Estimation for Transformer-based Language Models.

Ting Hua, Xiao Li, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

A Closer Look at Rehearsal-Free Continual Learning.

James Seale Smith, Junjiao Tian, Shaunak Halbe, Yen-Chang Hsu, Zsolt Kira

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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, Yen-Chang Hsu, Zsolt Kira

CoRR, 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

DictFormer: Tiny Transformer with Shared Dictionary.

Qian Lou, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Proceedings of the Tenth International Conference on Learning Representations, 2022

Language model compression with weighted low-rank factorization.

Yen-Chang Hsu, Ting Hua, Sungen Chang, Qian Lou, Yilin Shen, Hongxia Jin

Proceedings of the Tenth International Conference on Learning Representations, 2022

Numerical Optimizations for Weighted Low-rank Estimation on Language Models.

Ting Hua, Yen-Chang Hsu, Felicity Wang, Qian Lou, Yilin Shen, Hongxia Jin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Lite-MDETR: A Lightweight Multi-Modal Detector.

Qian Lou, Yen-Chang Hsu, Burak Uzkent, Ting Hua, Yilin Shen, Hongxia Jin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

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

A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition.

Junjiao Tian, Dylan Yung, Yen-Chang Hsu, Zsolt Kira

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding.

Ting Hua, Yilin Shen, Changsheng Zhao, Yen-Chang Hsu, Hongxia Jin

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer.

James Seale Smith, Jonathan Balloch, Yen-Chang Hsu, Zsolt Kira

Proceedings of the International Joint Conference on Neural Networks, 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

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU.

Yilin Shen, Yen-Chang Hsu, Avik Ray, Hongxia Jin

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Learning from pairwise similarity for visual categorization.

Yen-Chang Hsu

PhD thesis, 2020

Posterior Re-calibration for Imbalanced Datasets.

Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data.

Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Multi-class classification without multi-class labels.

Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines.

Yen-Chang Hsu, Yen-Cheng Liu, Zsolt Kira

CoRR, 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

Learning to Cluster for Proposal-Free Instance Segmentation.

Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Learning to cluster in order to transfer across domains and tasks.

Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira

Proceedings of the 6th International Conference on Learning Representations, 2018

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

Proceedings of the 6th International Conference on Learning Representations, 2018

Training Student Networks for Acceleration with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

Proceedings of the British Machine Vision Conference 2018, 2018

2017

Learning Loss for Knowledge Distillation with Conditional Adversarial Networks.

Zheng Xu, Yen-Chang Hsu, Jiawei Huang

CoRR, 2017

2016

Deep Image Category Discovery using a Transferred Similarity Function.

Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira

CoRR, 2016

2015

Neural network-based clustering using pairwise constraints.

Yen-Chang Hsu, Zsolt Kira

CoRR, 2015