Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas
Esther Rolf, Lucia Gordon, Milind Tambe, Andrew Davies
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Federico Ottomano, John Y. Goulermas, Vladimir Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa P. Manzanera, Emmeline G. Poole, Matthew S. Dyer, John B. Claridge, Jon Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin Sanderson, Matthew J. Rosseinsky
Material synthesis through simulations guided by machine learning: a position paper
Usman Syed, Federico Cunico, Uzair Khan, Eros Radicchi, Francesco Setti, Adolfo Speghini, Paolo Marone, Filiberto Semenzin, Marco Cristani
Topology optimization of periodic lattice structures for specified mechanical properties using machine learning considering member connectivity
Tomoya Matsuoka, Makoto Ohsaki, Kazuki Hayashi
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
Junae Kim, Amardeep Kaur
Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix
Subhasish Das, Satish K Panda, Madhusmita Sethy, Prajna Paramita Giri, Ashwini K Nanda
SoK: A Systems Perspective on Compound AI Threats and Countermeasures
Sarbartha Banerjee, Prateek Sahu, Mulong Luo, Anjo Vahldiek-Oberwagner, Neeraja J. Yadwadkar, Mohit Tiwari
Executable QR codes with Machine Learning for Industrial Applications
Stefano Scanzio, Francesco Velluto, Matteo Rosani, Lukasz Wisniewski, Gianluca Cena
Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe
Colverd Grace, Schade Laura, Takami Jumpei, Bot Karol, Gallego Joseph
Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics
Abraham Itzhak Weinberg, Alessio Faccia
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs
Md Ahsanul Kabir, Kareem Abdelfatah, Shushan He, Mohammed Korayem, Mohammad Al Hasan
Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization
Mohammad R. Salmanpour, Morteza Alizadeh, Ghazal Mousavi, Saba Sadeghi, Sajad Amiri, Mehrdad Oveisi, Arman Rahmim, Ilker Hacihaliloglu
Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning
Mir Faris, Syeda Aynul Karim, Md. Juniadul Islam
Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition
Haoyi Jia, Abhilasha Dave, Julia Gonski, Ryan Herbst
Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods
Baljinnyam Dayan
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification
Nathaniel Hanson, Philip Manke, Simon Birkholz, Maximilian Mühlbauer, Rene Heine, Arnd Brandes