1 citations · 1 across the 11 of their papers we have counts for
12 papers
Dynamic Shapley Computation
Xuan Yang, Hsi-Wen Chen, Ming-Syan Chen +1
Shapley-based data valuation provides a principled way to quantify the contribution of training data, but its high computational cost makes it impractical in dynamic settings where…
Interference-Aware Multi-Task Unlearning
Ying-Hua Huang, Rui Fang, Hsi-Wen Chen +1
Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data. Existing work mainly focuses…
LoopQ: Quantization for Looped Language Models
Rui Fang, Hsi-Wen Chen, Ming-Syan Chen +1
Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count. However, this reuse…
Amortized-Precision Quantization for Early-Exit Vision Transformers
Rui Fang, Hsi-Wen Chen, Ming-Syan Chen
Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Existing quantization methods assu…
Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation
Xuan Yang, Hsi-Wen Chen, Ming-Syan Chen +1
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global…
Self-guided Knowledgeable Network of Thoughts: Amplifying Reasoning with Large Language Models
Chao-Chi Chen, Chin-Yuan Yeh, Hsi-Wen Chen +2
We introduce Knowledgeable Network of Thoughts (kNoT): a prompt scheme that advances the capabilities of large language models (LLMs) beyond existing paradigms like Chain-of-Though…
Evaluating Adversarial Robustness in the Spatial Frequency Domain
Keng-Hsin Liao, Chin-Yuan Yeh, Hsi-Wen Chen +1
Convolutional Neural Networks (CNNs) have dominated the majority of computer vision tasks. However, CNNs' vulnerability to adversarial attacks has raised concerns about deploying t…
In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol
Wei-Han Wang, Chin-Yuan Yeh, Hsi-Wen Chen +2
As deep generative models advance, we anticipate deepfakes achieving "perfection"-generating no discernible artifacts or noise. However, current deepfake detectors, intentionally o…
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
Siyang Jiang, Rui Fang, Hsi-Wen Chen +2
Support-query shift few-shot learning aims to classify unseen examples (query set) to labeled data (support set) based on the learned embedding in a low-dimensional space under a d…
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Siyang Jiang, Wei Ding, Hsi-Wen Chen +1
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set. While these works assume that…
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack
Chin-Yuan Yeh, Hsi-Wen Chen, Hong-Han Shuai +2
With the successful creation of high-quality image-to-image (Img2Img) translation GANs comes the non-ethical applications of DeepFake and DeepNude. Such misuses of img2img techniqu…
A Unified Seeding Framework
Ya-Wen Teng, Hsi-Wen Chen, De-Nian Yang +3
Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds…