79 citations · 355 across the 59 of their papers we have counts for
9 papers · 1 filter
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models
Guihong Li, Hsiang Hsu, Chun-Fu Chen +1
The rapid growth of machine learning has spurred legislative initiatives such as ``the Right to be Forgotten,'' allowing users to request data removal. In response, ``machine unlea…
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
In recent years, medical image segmentation has become an important application in the field of computer-aided diagnosis. In this paper, we are the first to propose a new graph con…
Efficient Low-rank Backpropagation for Vision Transformer Adaptation
Yuedong Yang, Hung-Yueh Chiang, Guihong Li +2
The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications. This…
Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities
Guihong Li, Duc Hoang, Kartikeya Bhardwaj +3
Recently, zero-shot (or training-free) Neural Architecture Search (NAS) approaches have been proposed to liberate NAS from the expensive training process. The key idea behind zero-…
MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications
Mustafa Munir, William Avery, Radu Marculescu
Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provi…
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
Guihong Li, Kartikeya Bhardwaj, Yuedong Yang +1
Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints. However, the manuall…
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale self-atte…
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
Guihong Li, Yuedong Yang, Kartikeya Bhardwaj +1
Neural Architecture Search (NAS) is widely used to automatically obtain the neural network with the best performance among a large number of candidate architectures. To reduce the…
Efficient On-device Training via Gradient Filtering
Yuedong Yang, Guihong Li, Radu Marculescu
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the la…