23 citations · 121 across the 23 of their papers we have counts for
23 papers
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Ching-Hua Lee, Chouchang Yang, Jaejin Cho +4
Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The de…
FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
James Seale Smith, Chi-Heng Lin, Shikhar Tuli +5
The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-c…
DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
Shangqian Gao, Chi-Heng Lin, Ting Hua +4
Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, including language modeling, understanding, and generation. However, the…
MoDeGPT: Modular Decomposition for Large Language Model Compression
Chi-Heng Lin, Shangqian Gao, James Seale Smith +5
Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computationa…
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu +3
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we…
Prompt Tuning for Zero-shot Compositional Learning
Lingyu Zhang, Ting Hua, Yilin Shen +1
Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and obje…
Token Fusion: Bridging the Gap between Token Pruning and Token Merging
Minchul Kim, Shangqian Gao, Yen-Chang Hsu +2
Vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. However, their computational overhead, largely attributed to…
Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
James Seale Smith, Yen-Chang Hsu, Zsolt Kira +2
Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only…
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
Jiaqi Xue, Mengxin Zheng, Ting Hua +4
Large Language Models (LLMs) are progressively being utilized as machine learning services and interface tools for various applications. However, the security implications of LLMs,…
To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement
Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Ching-Hua Lee +3
Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happ…
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
James Seale Smith, Yen-Chang Hsu, Lingyu Zhang +4
Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such model…
GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer
Miao Yin, Burak Uzkent, Yilin Shen +2
The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foun…
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation
Kaiwen Zhou, Kaizhi Zheng, Connor Pryor +4
The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete…
Numerical Optimizations for Weighted Low-rank Estimation on Language Model
Ting Hua, Yen-Chang Hsu, Felicity Wang +3
Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameter…
Language model compression with weighted low-rank factorization
Yen-Chang Hsu, Ting Hua, Sungen Chang +3
Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, appr…
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients
Yen-Chang Hsu, James Smith, Yilin Shen +2
Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. A vast number of methods have been developed for…
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering
Peixi Xiong, Yilin Shen, Hongxia Jin
Learning to answer visual questions is a challenging task since the multi-modal inputs are within two feature spaces. Moreover, reasoning in visual question answering requires the…
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding
Ting Hua, Yilin Shen, Changsheng Zhao +2
Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it imp…
Automatic Mixed-Precision Quantization Search of BERT
Changsheng Zhao, Ting Hua, Yilin Shen +2
Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of paramet…
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
Junjiao Tian, Yen-Change Hsu, Yilin Shen +2
Moving beyond testing on in-distribution data works on Out-of-Distribution (OOD) detection have recently increased in popularity. A recent attempt to categorize OOD data introduces…
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU
Yilin Shen, Yen-Chang Hsu, Avik Ray +1
Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to d…
An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
Yu Wang, Yilin Shen, Hongxia Jin
Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/ad…
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
James Smith, Yen-Chang Hsu, Jonathan Balloch +3
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgett…