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
Social Cue Detection and Analysis Using Transfer Entropy
Haoyang Jiang, Elizabeth A. Croft, Michael G. Burke
A Topological Distance Measure between Multi-Fields for Classification and Analysis of Shapes and Data
Yashwanth Ramamurthi, Amit Chattopadhyay
An Analysis of Physics-Informed Neural Networks
Edward Small
Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis
Rajesh Shrestha
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions
Sayantan Choudhury, Eduard Gorbunov, Nicolas Loizou
Systematic Rectification of Language Models via Dead-end Analysis
Meng Cao, Mehdi Fatemi, Jackie Chi Kit Cheung, Samira Shabanian
Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline
Siddhesh Khandelwal, Anirudth Nambirajan, Behjat Siddiquie, Jayan Eledath, Leonid Sigal
The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation
Kushal Arora, Timothy J. O'Donnell, Doina Precup, Jason Weston, Jackie C. K. Cheung