Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Chen, Zexi"'
This paper advances a novel architectural schema anchored upon the Transformer paradigm and innovatively amalgamates the K-means categorization algorithm to augment the contextual apprehension capabilities of the schema. The transformer model perform
Externí odkaz:
http://arxiv.org/abs/2408.04216
The prediction of disease risk factors can screen vulnerable groups for effective prevention and treatment, so as to reduce their morbidity and mortality. Machine learning has a great demand for high-quality labeling information, and labeling noise i
Externí odkaz:
http://arxiv.org/abs/2406.16982
Most of the existing wavelet image processing techniques are carried out in the form of single-scale reconstruction and multiple iterations. However, processing high-quality fMRI data presents problems such as mixed noise and excessive computation ti
Externí odkaz:
http://arxiv.org/abs/2406.16981
In this research, we introduce an innovative method for synthesizing medical images using generative adversarial networks (GANs). Our proposed GANs method demonstrates the capability to produce realistic synthetic images even when trained on a limite
Externí odkaz:
http://arxiv.org/abs/2406.18547
The research explores the utilization of a deep learning model employing an attention mechanism in medical text mining. It targets the challenge of analyzing unstructured text information within medical data. This research seeks to enhance the model'
Externí odkaz:
http://arxiv.org/abs/2406.00016
Autor:
Friedman, Luke, Ahuja, Sameer, Allen, David, Tan, Zhenning, Sidahmed, Hakim, Long, Changbo, Xie, Jun, Schubiner, Gabriel, Patel, Ajay, Lara, Harsh, Chu, Brian, Chen, Zexi, Tiwari, Manoj
A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ab
Externí odkaz:
http://arxiv.org/abs/2305.07961
Autor:
Chen, Zexi, Liao, Yiyi, Du, Haozhe, Zhang, Haodong, Xu, Xuecheng, Lu, Haojian, Xiong, Rong, Wang, Yue
Pose registration is critical in vision and robotics. This paper focuses on the challenging task of initialization-free pose registration up to 7DoF for homogeneous and heterogeneous measurements. While recent learning-based methods show promise usin
Externí odkaz:
http://arxiv.org/abs/2206.05707
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of
Externí odkaz:
http://arxiv.org/abs/2204.07992
The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map. In this paper, we propose an efficient least square based depth-independent method to complete the sparse depth map utilizing the RGB image and the sparse
Externí odkaz:
http://arxiv.org/abs/2203.03317
Autor:
Yang, Shaolin, Fu, Panpan, Chen, Zexi, Chen, Ya, Feng, Yajuan, Wu, Jiandong, Lu, Hui, Hou, Chunping
Publikováno v:
In Journal of Power Sources 15 December 2024 623
