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
Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison
Simone Göttlich, Jacob Heieck, Andreas Neuenkirch
Explaining Spectrograms in Machine Learning: A Study on Neural Networks for Speech Classification
Jesin James, Balamurali B. T., Binu Abeysinghe, Junchen Liu
Analyzing Machine Learning Performance in a Hybrid Quantum Computing and HPC Environment
Samuel T. Bieberich, Michael A. Sandoval
High-Throughput Phenotyping using Computer Vision and Machine Learning
Vivaan Singhvi, Langalibalele Lunga, Pragya Nidhi, Chris Keum, Varrun Prakash
Fostering Trust and Quantifying Value of AI and ML
Dalmo Cirne, Veena Calambur
Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization
Pallavi Mitra, Felix Biessmann
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery
Kun Wu, Zixu Wang, Xiulong Yang, Yangyang Chen, Zhenqi Han, Jialu Zhang, Lizhuang Liu
A Theory of Machine Learning
Jinsook Kim, Jinho Kang
Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine information
Vishnu S. Pendyala, Madhulika Dutta
Quantum Dynamics of Machine Learning
Peng Wang, Maimaitiniyazi Maimaitiabudula
Detecting new obfuscated malware variants: A lightweight and interpretable machine learning approach
Oladipo A. Madamidola, Felix Ngobigha, Adnane Ez-zizi
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
An AI Architecture with the Capability to Classify and Explain Hardware Trojans
Paul Whitten, Francis Wolff, Chris Papachristou
Introducing 'Inside' Out of Distribution
Teddy Lazebnik
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method
Zhikun Zhang, Yiting Duan, Xiangjun Wang, Mingyuan Zhang
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection
Omer Subasi, Johnathan Cree, Joseph Manzano, Elena Peterson
Bias Correction in Machine Learning-based Classification of Rare Events
Luuk Gubbels, Marco Puts, Piet Daas
Machine Learning for Economic Forecasting: An Application to China's GDP Growth
Yanqing Yang, Xingcheng Xu, Jinfeng Ge, Yan Xu