5 citations · 17 across the 32 of their papers we have counts for
13 papers · 1 filter
Cauchy-Schwarz Fairness Regularizer
Yezi Liu, Hanning Chen, Wenjun Huang +2
Group fairness in machine learning is often enforced by adding a regularizer that reduces the dependence between model predictions and sensitive attributes. However, existing regul…
Mitigating Bias in Graph Hyperdimensional Computing
Yezi Liu, William Youngwoo Chung, Yang Ni +2
Graph hyperdimensional computing (HDC) has emerged as a promising paradigm for cognitive tasks, emulating brain-like computation with high-dimensional vectors known as hypervectors…
LUNE: Efficient LLM Unlearning via LoRA Fine-Tuning with Negative Examples
Yezi Liu, Hanning Chen, Wenjun Huang +2
Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes i…
Recover-to-Forget: Gradient Reconstruction from LoRA for Efficient LLM Unlearning
Yezi Liu, Hanning Chen, Wenjun Huang +2
Unlearning in large foundation models (e.g., LLMs) is essential for enabling dynamic knowledge updates, enforcing data deletion rights, and correcting model behavior. However, exis…
Encoder-Free Knowledge-Graph Reasoning with LLMs via Hyperdimensional Path Retrieval
Yezi Liu, William Youngwoo Chung, Hanning Chen +2
Recent progress in large language models (LLMs) has made knowledge-grounded reasoning increasingly practical, yet KG-based QA systems often pay a steep price in efficiency and tran…
QUILL: An Algorithm-Architecture Co-Design for Cache-Local Deformable Attention
Hyunwoo Oh, Hanning Chen, Sanggeon Yun +5
Deformable transformers deliver state-of-the-art detection but map poorly to hardware due to irregular memory access and low arithmetic intensity. We introduce QUILL, a schedule-aw…
T-SAR: A Full-Stack Co-design for CPU-Only Ternary LLM Inference via In-Place SIMD ALU Reorganization
Hyunwoo Oh, KyungIn Nam, Rajat Bhattacharjya +7
Recent advances in LLMs have outpaced the computational and memory capacities of edge platforms that primarily employ CPUs, thereby challenging efficient and scalable deployment. W…
ASTER: Attention-based Spiking Transformer Engine for Event-driven Reasoning
Tamoghno Das, Khanh Phan Vu, Hanning Chen +2
The integration of spiking neural networks (SNNs) with transformer-based architectures has opened new opportunities for bio-inspired low-power, event-driven visual reasoning on edg…
Draft and Refine with Visual Experts
Sungheon Jeong, Ryozo Masukawa, Jihong Park +5
While recent Large Vision-Language Models (LVLMs) exhibit strong multimodal reasoning abilities, they often produce ungrounded or hallucinated responses because they rely too heavi…
Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing
Yezi Liu, Prathyush Poduval, Wenjun Huang +3
Graph unlearning is a crucial approach for protecting user privacy by erasing the influence of user data on trained graph models. Recent developments in graph unlearning methods ha…
LVLM_CSP: Accelerating Large Vision Language Models via Clustering, Scattering, and Pruning for Reasoning Segmentation
Hanning Chen, Yang Ni, Wenjun Huang +4
Large Vision Language Models (LVLMs) have been widely adopted to guide vision foundation models in performing reasoning segmentation tasks, achieving impressive performance. Howeve…
Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge
Sanggeon Yun, Ryozo Masukawa, Hanning Chen +6
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computation…
Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
Zhiling Chen, Hanning Chen, Mohsen Imani +1
In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (…