Zobrazeno 1 - 10
of 1 004
pro vyhledávání: '"Chen, Badong"'
Recently, multi-view learning has witnessed a considerable interest on the research of trusted decision-making. Previous methods are mainly inspired from an important paper published by Han et al. in 2021, which formulates a Trusted Multi-view Classi
Externí odkaz:
http://arxiv.org/abs/2411.03713
Source-free domain generalization (SFDG) tackles the challenge of adapting models to unseen target domains without access to source domain data. To deal with this challenging task, recent advances in SFDG have primarily focused on leveraging the text
Externí odkaz:
http://arxiv.org/abs/2409.14163
Autor:
Li, Yuanhao, Chen, Badong, Hu, Zhongxu, Suzuki, Keita, Bai, Wenjun, Koike, Yasuharu, Yamashita, Okito
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function
Externí odkaz:
http://arxiv.org/abs/2408.14843
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge lies in effectively combining diverse views, where each view has distin
Externí odkaz:
http://arxiv.org/abs/2408.07331
This paper presents a general scheme for enhancing the convergence and performance of DETR (DEtection TRansformer). We investigate the slow convergence problem in transformers from a new perspective, suggesting that it arises from the self-attention
Externí odkaz:
http://arxiv.org/abs/2407.11699
We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from G\'acs-K\"orner common information in information theory. Leveraging this definition, we develop a novel
Externí odkaz:
http://arxiv.org/abs/2406.15043
With the advancement of neural networks, diverse methods for neural Granger causality have emerged, which demonstrate proficiency in handling complex data, and nonlinear relationships. However, the existing framework of neural Granger causality has s
Externí odkaz:
http://arxiv.org/abs/2405.08779
Divergence measures play a central role and become increasingly essential in deep learning, yet efficient measures for multiple (more than two) distributions are rarely explored. This becomes particularly crucial in areas where the simultaneous manag
Externí odkaz:
http://arxiv.org/abs/2405.04061
Large pre-trained vision language models (VLMs) have shown impressive zero-shot ability on downstream tasks with manually designed prompt. To further adapt VLMs to downstream tasks, soft prompt is proposed to replace manually designed prompt, which u
Externí odkaz:
http://arxiv.org/abs/2404.19286
Pretrained vision-language models (VLMs) like CLIP exhibit exceptional generalization across diverse downstream tasks. While recent studies reveal their vulnerability to adversarial attacks, research to date has primarily focused on enhancing the rob
Externí odkaz:
http://arxiv.org/abs/2404.19287