Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Deng Wenlong"'
Autor:
Deng, Wenlong, Zhao, Yize, Vakilian, Vala, Chen, Minghui, Li, Xiaoxiao, Thrampoulidis, Christos
Storing open-source fine-tuned models separately introduces redundancy and increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), particularly the random drop and rescale (DARE) method proposed by Yu et al.
Externí odkaz:
http://arxiv.org/abs/2410.09344
In the era of Foundation Models' (FMs) rising prominence in AI, our study addresses the challenge of biases in medical images while the model operates in black-box (e.g., using FM API), particularly spurious correlations between pixels and sensitive
Externí odkaz:
http://arxiv.org/abs/2403.06104
Mitigating biases in machine learning models has become an increasing concern in Natural Language Processing (NLP), particularly in developing fair text embeddings, which are crucial yet challenging for real-world applications like search engines. In
Externí odkaz:
http://arxiv.org/abs/2402.14208
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. This suggests a promising paradigm shift of adapting pre-trained ViT models to Federated Learning
Externí odkaz:
http://arxiv.org/abs/2310.18285
Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that affects social communication and behavior. Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and dia
Externí odkaz:
http://arxiv.org/abs/2307.10181
In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggr
Externí odkaz:
http://arxiv.org/abs/2306.03407
Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, which is a crucial yet challenging
Externí odkaz:
http://arxiv.org/abs/2301.01481
In light of the smoothness property brought by skip connections in ResNet, this paper proposed the Skip Logit to introduce the skip connection mechanism that fits arbitrary DNN dimensions and embraces similar properties to ResNet. Meta Tanh Normaliza
Externí odkaz:
http://arxiv.org/abs/2210.10725
Autor:
Zhao, Yuantao, Yue, Yongkang, Deng, Wenlong, Li, Jiansheng, Chen, Ming, Liu, Shenqiang, Li, Wenge, Liu, Yanbo, Ji, Vincent
Publikováno v:
In Journal of Materials Research and Technology November-December 2024 33:1155-1164
Publikováno v:
In Applied Surface Science 15 July 2024 661