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

May 11, 2024

Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern
M. Sajid, Rahul Sharma, Iman Beheshti, M. Tanveer
Machine Learning Medical Diagnosis Comprehensive Evaluation Memory Management Disease Neuroimaging Initiative
Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis
Ramit Debnath, Pengyu Zhang, Tianzhu Qin, R. Michael Alvarez, Shaun D. Fitzgerald
Machine Learning Climate Change
Reimplementation of Learning to Reweight Examples for Robust Deep Learning
Parth Patil, Ben Boardley, Jack Gardner, Emily Loiselle, Deerajkumar Parthipan
Machine Learning LeArning Abstract Deep Neural Network Generalization Performance Robust Deep Skin Cancer Detection Text Reuse

May 10, 2024

Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare
Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang
Machine Learning Foundation Model Federated Learning Emerging Opportunity Open Challenge Biomedical Research Sensitive Patient Complex Model Federated Foundation Model
The Role of Learning Algorithms in Collective Action
Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
Machine Learning Integral Role Machine Learning Algorithm Learning Algorithm Optimal Classifier Bayes Classifier Collective Control
A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments
Joyce Fonteles, Eduardo Davalos, Ashwin T. S., Yike Zhang, Mengxi Zhou, Efrat Ayalon, Alicia Lane, Selena Steinberg, Gabriella Anton, Joshua Danish, Noel Enyedy, Gautam Biswas
Machine Learning Multimodal Analysis Temporal Learning First Step Scientific Communication Interaction Evaluation MultiModal Learning
Detecting Moving Objects With Machine Learning
Wesley C. Fraser
Machine Learning Image Sequence Astronomical Image Moving Object Detection RSO Detection

May 9, 2024

LLMs for XAI: Future Directions for Explaining Explanations
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
Large Language Model Machine Learning Inherent Interpretability Line by Line Explanation Explainable Artificial Intelligence Future Direction xAI Community Explanation Selection
Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas
Azarakhsh Jalalvand, SangKyeun Kim, Jaemin Seo, Qiming Hu, Max Curie, Peter Steiner, Andrew Oakleigh Nelson, Yong-Su Na, Egemen Kolemen
Machine Learning Application Proficiency Super Resolution Plasma Dynamic Fusion Plasma Hidden Physic Super Resolution Algorithm Magnetic Perturbation
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
Amin Aminifar, Matin Shokri, Amir Aminifar
Machine Learning Federated Edge Learning Privacy Preserving Federated Learning Intelligent Healthcare Wearable Technology
Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning
Fengyi Gao, Xingyu Zhang, Sonish Sivarajkumar, Parker Denny, Bayan Aldhahwani, Shyam Visweswaran, Ryan Shi, William Hogan, Allyn Bove, Yanshan Wang
Machine Learning Electronic Health Record Functional Outcome Post Stroke
Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
Yuqi Zhou, Hao Zhu
Machine Learning Potential Scalability Power Grid Load Shedding

May 8, 2024

Automated Program Repair: Emerging trends pose and expose problems for benchmarks
Joseph Renzullo, Pemma Reiter, Westley Weimer, Stephanie Forrest
Machine Learning New Benchmark Neural Machine Translation Recent Trend Related Problem Program Repair
CloudSense: A Model for Cloud Type Identification using Machine Learning from Radar data
Mehzooz Nizar, Jha K. Ambuj, Manmeet Singh, Vaisakh S. B, G. Pandithurai
Machine Learning Full Model Radar Data Cloud Detection Precipitation Estimation Cloud Classification
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep, Andrew Vlasic, Richard Padbury, Anh Pham
Machine Learning Molecular Property Prediction Molecular Descriptor Deep Quantum Quantum Feature Map Quantum Graph Neural Network
Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules
Vanni Doffini, O. Anatole von Lilienfeld, Michael A. Nash
Machine Learning Small Molecule Peptide Source Discrete Space Phenotype Prediction Error Scaling
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
Alice Cicirello
Machine Learning Dynamical System Physic Informed Machine Learning Scientific Machine Learning Complex Dynamical System
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
Chancellor R. Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy
Large Language Model Neural Network Machine Learning Synthetic Data System Performance Synthetic Training Data

May 7, 2024

Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs
Antonio Bikić, Sayan Mukherjee
Neural Network Machine Learning General Principle Artificial Neural Network Automatic Usefulness Prediction
Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs
Martin Marzidovšek, Janja Francé, Vid Podpečan, Stanka Vadnjal, Jožica Dolenc, Patricija Mozetič
Machine Learning Long Term Predictive Performance Interpretable Machine Harmful Algal Bloom

Machine Learning

Papers

Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern

Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis

Reimplementation of Learning to Reweight Examples for Robust Deep Learning

Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

The Role of Learning Algorithms in Collective Action

A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments

Detecting Moving Objects With Machine Learning

LLMs for XAI: Future Directions for Explaining Explanations

Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems

Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Automated Program Repair: Emerging trends pose and expose problems for benchmarks

CloudSense: A Model for Cloud Type Identification using Machine Learning from Radar data

Hybrid Quantum Graph Neural Network for Molecular Property Prediction

Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules

Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations

Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks

Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs

Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs