3 accepted papers
Fine-tuning large language models (LLMs) is difficult due to their huge model size. Recent Fourier domain-based methods show potential for reducing fine-tuning costs. We propose a block circulant matrix-based fine-tuning method with a stable training heuristic to leverage the properties of circulant
In-orbit object detection is essential for Earth observation missions on satellites equipped with GPUs. A promising approach is to use pre-trained vision-language modeling (VLM) to enhance its open-vocabulary capability. However, adopting it on satellites poses two challenges: (1) satellite imagery…
Despite the great success in Natural Language Processing (NLP) area, large pre-trained language models like BERT are not well-suited for resource-constrained or real-time applications owing to the large number of parameters and slow inference speed. Recently, compressing and accelerating BERT have b…