Zobrazeno 1 - 10
of 8 797
pro vyhledávání: '"Yalin, A. P."'
Autor:
Du, Xinkai, Han, Quanjie, Lv, Chao, Liu, Yan, Sun, Yalin, Shu, Hao, Shan, Hongbo, Sun, Maosong
Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs). However, there is a lack of definitive labels ava
Externí odkaz:
http://arxiv.org/abs/2412.18800
Autor:
Wang, Hao, Zhu, Wenhui, Dong, Xuanzhao, Chen, Yanxi, Li, Xin, Qiu, Peijie, Chen, Xiwen, Vasa, Vamsi Krishna, Xiong, Yujian, Dumitrascu, Oana M., Razi, Abolfazl, Wang, Yalin
In this work, we propose Many-MobileNet, an efficient model fusion strategy for retinal disease classification using lightweight CNN architecture. Our method addresses key challenges such as overfitting and limited dataset variability by training mul
Externí odkaz:
http://arxiv.org/abs/2412.02825
In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then transmitted over a wireless channel. At the receiver, a decoder performs a
Externí odkaz:
http://arxiv.org/abs/2411.10385
Matrix factor models have been growing popular dimension reduction tools for large-dimensional matrix time series. However, the heteroscedasticity of the idiosyncratic components has barely received any attention. Starting from the pseudo likelihood
Externí odkaz:
http://arxiv.org/abs/2411.06423
Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the
Externí odkaz:
http://arxiv.org/abs/2411.01403
Autor:
Farazi, Mohammad, Wang, Yalin
Utilizing patch-based transformers for unstructured geometric data such as polygon meshes presents significant challenges, primarily due to the absence of a canonical ordering and variations in input sizes. Prior approaches to handling 3D meshes and
Externí odkaz:
http://arxiv.org/abs/2411.00164
Radio frequency (RF) communication has been an important part of civil and military communication for decades. With the increasing complexity of wireless environments and the growing number of devices sharing the spectrum, it has become critical to e
Externí odkaz:
http://arxiv.org/abs/2410.18283
With the rapid development of deep learning, CNN-based U-shaped networks have succeeded in medical image segmentation and are widely applied for various tasks. However, their limitations in capturing global features hinder their performance in comple
Externí odkaz:
http://arxiv.org/abs/2410.15036
Deep Reinforcement Learning (DRL) has been highly effective in learning from and adapting to RF environments and thus detecting and mitigating jamming effects to facilitate reliable wireless communications. However, traditional DRL methods are suscep
Externí odkaz:
http://arxiv.org/abs/2410.10521
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a U-shaped architecture (UNet) with skip connections have been adopted f
Externí odkaz:
http://arxiv.org/abs/2410.11578