5 accepted papers
Vertical Federated Learning (VFL) involves multiple participants collaborating to train machine learning models on distinct feature sets from the same data samples. This training paradigm with distributed updating focuses on secure and efficient communication. Nevertheless, the trained models exhibi
Spiking large language models (LLMs) offer significant advantages on neuromorphic hardware, yet training them from scratch remains prohibitively expensive. A promising alternative is ANN-to-SNN conversion, which reuses pretrained ANN weights while minimizing conversion error. However, existing conv…
Fine-tuning large language models (LLMs) has achieved significant success in downstream tasks. However, as the model size continues to grow, traditional fine-tuning methods have become increasingly impractical due to their high computational and memory costs. This has motivated researchers to explor…
Black-box prompt tuning has become a prevalent parameter-efficient paradigm that leverages the capabilities of large language models (LLMs) for customized applications in specific downstream tasks. In practical scenarios, downstream tasks frequently involve data distributions that are heavily imbala…
Visual Prompt Learning (VPL) has emerged as a powerful strategy for harnessing the capabilities of large-scale pre-trained models (PTMs) to tackle specific downstream tasks. However, the opaque nature of PTMs in many real-world applications has led to a growing interest in gradient-free approaches w…