General Analysis
General analysis encompasses a broad range of methodologies applied across diverse scientific domains to extract meaningful insights from data. Current research focuses on developing robust and efficient analytical techniques, including the application of machine learning models like convolutional neural networks, graph neural networks, and transformer architectures, as well as statistical methods for data modeling and hypothesis testing. These advancements are improving the accuracy and efficiency of analyses in fields ranging from medical image processing and materials science to social media analysis and autonomous systems, ultimately leading to more reliable scientific findings and improved decision-making in various applications.
Papers
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
Rolf A. N. Starre, Marco Loog, Elena Congeduti, Frans A. Oliehoek
Analysis of Distributed Deep Learning in the Cloud
Aakash Sharma, Vivek M. Bhasi, Sonali Singh, Rishabh Jain, Jashwant Raj Gunasekaran, Subrata Mitra, Mahmut Taylan Kandemir, George Kesidis, Chita R. Das
The Analysis about Building Cross-lingual Sememe Knowledge Base Based on Deep Clustering Network
Xiaoran Li, Toshiaki Takano
Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty
Paul Trust, Ahmed Zahran, Rosane Minghim
Comparison and Analysis of New Curriculum Criteria for End-to-End ASR
Georgios Karakasidis, Tamás Grósz, Mikko Kurimo