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
Analysis of learning a flow-based generative model from limited sample complexity
Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
${\tt MORALS}$: Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space
Ewerton R. Vieira, Aravind Sivaramakrishnan, Sumanth Tangirala, Edgar Granados, Konstantin Mischaikow, Kostas E. Bekris
Comparative Study and Framework for Automated Summariser Evaluation: LangChain and Hybrid Algorithms
Bagiya Lakshmi S, Sanjjushri Varshini R, Rohith Mahadevan, Raja CSP Raman
Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis
Han Zhang, Qiguang Chen, Lok Ming Lui
Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques
Cristiana Bolchini, Luca Cassano, Antonio Miele
Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision
Naveen Kanigiri, Manohar Suggula, Debanjali Bhattacharya, Neelam Sinha
Analysis on Multi-robot Relative 6-DOF Pose Estimation Error Based on UWB Range
Xinran Li, Shuaikang Zheng, Pengcheng Zheng, Haifeng Zhang, Zhitian Li, Xudong Zou