Zobrazeno 1 - 10
of 45
pro vyhledávání: '"68T07, 68T05"'
Artificial Neural Networks (ANNs) have significantly advanced various fields by effectively recognizing patterns and solving complex problems. Despite these advancements, their interpretability remains a critical challenge, especially in applications
Externí odkaz:
http://arxiv.org/abs/2409.11078
The unwavering success of deep learning in the past decade led to the increasing prevalence of deep learning methods in various application fields. However, the downsides of deep learning, most prominently its lack of trustworthiness, may not be comp
Externí odkaz:
http://arxiv.org/abs/2408.06212
Autor:
Shehzad, Ahsan, Xia, Feng, Abid, Shagufta, Peng, Ciyuan, Yu, Shuo, Zhang, Dongyu, Verspoor, Karin
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility across vario
Externí odkaz:
http://arxiv.org/abs/2407.09777
The human brain is a complex, dynamic network, which is commonly studied using functional magnetic resonance imaging (fMRI) and modeled as network of Regions of interest (ROIs) for understanding various brain functions. Recent studies utilize deep le
Externí odkaz:
http://arxiv.org/abs/2406.17086
Autor:
Xu, Zexing, Zhang, Linjun, Yang, Sitan, Etesami, Rasoul, Tong, Hanghang, Zhang, Huan, Han, Jiawei
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data from these peak periods poses a significant challenge for traditional f
Externí odkaz:
http://arxiv.org/abs/2406.16221
The proliferation of extensive neural network architectures, particularly deep learning models, presents a challenge in terms of resource-intensive training. GPU memory constraints have become a notable bottleneck in training such sizable models. Exi
Externí odkaz:
http://arxiv.org/abs/2403.11204
Autor:
Guastavino, Sabrina, Bahamazava, Katsiaryna, Perracchione, Emma, Camattari, Fabiana, Audone, Gianluca, Telloni, Daniele, Susino, Roberto, Nicolini, Gianalfredo, Fineschi, Silvano, Piana, Michele, Massone, Anna Maria
This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques. Specifically, the Long-Short Term Memory recurrent neural network, which is particularly suited for application over long time series, is emplo
Externí odkaz:
http://arxiv.org/abs/2403.09847
Transferring knowledge in cross-domain reinforcement learning is a challenging setting in which learning is accelerated by reusing knowledge from a task with different observation and/or action space. However, it is often necessary to carefully selec
Externí odkaz:
http://arxiv.org/abs/2312.03764
Autor:
Bastounis, Alexander, Gorban, Alexander N., Hansen, Anders C., Higham, Desmond J., Prokhorov, Danil, Sutton, Oliver, Tyukin, Ivan Y., Zhou, Qinghua
In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-agnostic framework and algorithms minimising empirical risks and poten
Externí odkaz:
http://arxiv.org/abs/2309.07072
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural Networks has fostered a substantial interest in predicting clinical o
Externí odkaz:
http://arxiv.org/abs/2309.01941