Zobrazeno 1 - 10
of 39 346
pro vyhledávání: '"An, Changqing"'
Previous studies have demonstrated that emotional features from a single acoustic sentiment label can enhance depression diagnosis accuracy. Additionally, according to the Emotion Context-Insensitivity theory and our pilot study, individuals with dep
Externí odkaz:
http://arxiv.org/abs/2412.18614
Autor:
Teng, Changqing, Li, Guanglian
Current deep learning-based calibration schemes for rough volatility models are based on the supervised learning framework, which can be costly due to a large amount of training data being generated. In this work, we propose a novel unsupervised lear
Externí odkaz:
http://arxiv.org/abs/2412.02135
In the application of brain-computer interface (BCI), while pursuing accurate decoding of brain signals, we also need consider the computational efficiency of BCI devices. ECoG signals are multi-channel temporal signals which is collected using a hig
Externí odkaz:
http://arxiv.org/abs/2412.02078
In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current res
Externí odkaz:
http://arxiv.org/abs/2412.00378
Autor:
Wang, Cong, Yang, Weizhe, Wang, Haiping, Yang, Renjie, Li, Jing, Wang, Zhijun, Yu, Xinyao, Wei, Yixiong, Huang, Xianli, Liu, Zhaoyang, Zou, Changqing, Zhao, Zhifeng
This paper introduces a Physics-Informed model architecture that can be adapted to various backbone networks. The model incorporates physical information as additional input and is constrained by a Physics-Informed loss function. Experimental results
Externí odkaz:
http://arxiv.org/abs/2412.00087
Autor:
Dong, Linwei, Fan, Qingnan, Guo, Yihong, Wang, Zhonghao, Zhang, Qi, Chen, Jinwei, Luo, Yawei, Zou, Changqing
Pre-trained text-to-image diffusion models are increasingly applied to real-world image super-resolution (Real-ISR) task. Given the iterative refinement nature of diffusion models, most existing approaches are computationally expensive. While methods
Externí odkaz:
http://arxiv.org/abs/2411.18263
This paper introduces MotionLLaMA, a unified framework for motion synthesis and comprehension, along with a novel full-body motion tokenizer called the HoMi Tokenizer. MotionLLaMA is developed based on three core principles. First, it establishes a p
Externí odkaz:
http://arxiv.org/abs/2411.17335
Autor:
JI, Changqing
In the application of brain-computer interface (BCI), we not only need to accurately decode brain signals,but also need to consider the explainability of the decoding process, which is related to the reliability of the model. In the process of design
Externí odkaz:
http://arxiv.org/abs/2411.16165
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers (e.g., data from the web or other datasets) in training. However,
Externí odkaz:
http://arxiv.org/abs/2411.14049
Autor:
Luo, Xinchen, Cao, Jiangxia, Sun, Tianyu, Yu, Jinkai, Huang, Rui, Yuan, Wei, Lin, Hezheng, Zheng, Yichen, Wang, Shiyao, Hu, Qigen, Qiu, Changqing, Zhang, Jiaqi, Zhang, Xu, Yan, Zhiheng, Zhang, Jingming, Zhang, Simin, Wen, Mingxing, Liu, Zhaojie, Gai, Kun, Zhou, Guorui
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling. In industry, a wide-used modeling architecture is a cascading para
Externí odkaz:
http://arxiv.org/abs/2411.11739