Tag: 2024
All the talks with the tag "2024".
AI in Cybersecurity
Divy AgnihotriPublished: at 02:00 PMThis paper presents an overview of AI-driven cybersecurity, highlighting the use of machine learning, deep learning, natural language processing, and knowledge-based expert systems. It discusses the role of AI in protecting Internet-connected systems from cyber threats and attacks. The paper provides insights into the security intelligence modeling based on AI methods and identifies future research directions in the field.
Quantum Machine Learning — An Overview
Kirtidev ParidaPublished: at 02:00 PMThis paper provides an overview of quantum machine learning (QML) and its potential for computational acceleration. It discusses the current state of QML, benchmarking the performance of classical and quantum algorithms on various tasks. The paper highlights the use of hybrid methods and novel approaches in QML, demonstrating their promise in predicting quantum states and enhancing classical data science algorithms.
Application of a New Machine Learning Model to Improve Earthquake Ground Motion Predictions
Dibyanshu MohapatraPublished: at 02:00 PMThis paper presents a cross-region prediction model named SeisEML for predicting peak ground acceleration (PGA) during earthquakes. The SeisEML model combines hybridized models, kernel-based algorithms, tree regression algorithms, and regression algorithms to achieve improved accuracy compared to conventional attenuation relations. The model has been tested on datasets from Japan and Iran, demonstrating its potential for regional and global earthquake predictions.
LightGlue - Local Feature Matching at Light Speed
Adyasha M.Published: at 02:00 PMThis paper introduces LightGlue, a deep neural network that learns to match local features across images. It presents improvements over the state-of-the-art sparse matching method, SuperGlue, making LightGlue more efficient, accurate, and easier to train. The paper provides the implementation code for LightGlue.
Graph Neural Networks - Privacy and Applications
Rucha Bhalchandra JoshiPublished: at 02:00 PMThis work discusses the complex relationships within graph-structured data and the use of graph neural networks (GNNs) for tasks such as node classification and link prediction. It also addresses privacy concerns in GNNs and presents a privacy-preserving approach that safeguards local graph structures while enabling meaningful analysis and insights.
Characterizing Graph Datasets for Node Classification - Homophily-Heterophily Dichotomy and Beyond
Sikta MohantyPublished: at 02:00 PMThis work explores the concept of homophily in graph datasets and proposes a measure called adjusted homophily. It also introduces a new characteristic called label informativeness (LI) to distinguish different types of heterophily. The study shows that LI better correlates with graph neural network performance compared to traditional homophily measures.