19 citations · 22 across the 7 of their papers we have counts for
7 papers
Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation
Xiwen Wei, Guihong Li, Radu Marculescu
Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries. Thi…
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-…
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…
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…
FLASH: Fast Neural Architecture Search with Hardware Optimization
Guihong Li, Sumit K. Mandal, Umit Y. Ogras +1
Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications g…