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
LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement
H. Emre Erdem, Henry Leung
A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
Shivika Prasanna, Ajay Kumar, Deepthi Rao, Eduardo Simoes, Praveen Rao
Be aware of overfitting by hyperparameter optimization!
Igor V. Tetko, Ruud van Deursen, Guillaume Godin
Two-Phase Segmentation Approach for Accurate Left Ventricle Segmentation in Cardiac MRI using Machine Learning
Maria Tamoor, Abbas Raza Ali, Philemon Philip, Ruqqayia Adil, Rabia Shahid, Asma Naseer
Machine Learning for predicting chaotic systems
Christof Schötz, Alistair White, Maximilian Gelbrecht, Niklas Boers
Reconstructing Global Daily CO2 Emissions via Machine Learning
Tao Li, Lixing Wang, Zihan Qiu, Philippe Ciais, Taochun Sun, Matthew W. Jones, Robbie M. Andrew, Glen P. Peters, Piyu ke, Xiaoting Huang, Robert B. Jackson, Zhu Liu
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals
Sergio Caprioli, Jacopo Foschi, Riccardo Crupi, Alessandro Sabatino
Monetizing Currency Pair Sentiments through LLM Explainability
Lior Limonad, Fabiana Fournier, Juan Manuel Vera Díaz, Inna Skarbovsky, Shlomit Gur, Raquel Lazcano
Boosted generalized normal distributions: Integrating machine learning with operations knowledge
Ragip Gurlek, Francis de Vericourt, Donald K. K. Lee
GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
Yuchen Shen, Barnabás Póczos
Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis
Dalila Ressi, Alvise Spanò, Lorenzo Benetollo, Carla Piazza, Michele Bugliesi, Sabina Rossi
A data balancing approach towards design of an expert system for Heart Disease Prediction
Rahul Karmakar, Udita Ghosh, Arpita Pal, Sattwiki Dey, Debraj Malik, Priyabrata Sain
MLtoGAI: Semantic Web based with Machine Learning for Enhanced Disease Prediction and Personalized Recommendations using Generative AI
Shyam Dongre, Ritesh Chandra, Sonali Agarwal
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie, Stefan Denner, Lucas S. Brunschwig, Klaus Maier-Hein, Mihaela van der Schaar
StraightLine: An End-to-End Resource-Aware Scheduler for Machine Learning Application Requests
Cheng-Wei Ching, Boyuan Guan, Hailu Xu, Liting Hu
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Nicolas Hayer, Thorsten Wendel, Stephan Mandt, Hans Hasse, Fabian Jirasek
Machine Learning for Equitable Load Shedding: Real-time Solution via Learning Binding Constraints
Yuqi Zhou, Joseph Severino, Sanjana Vijayshankar, Juliette Ugirumurera, Jibo Sanyal
EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
Yiming Ma, Xinjie Li, Xin Sun, Zhiyong Wang, Lionel Z. Wang
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation
Saideep Kilaru, Kothamasu Jayachandra, Tanishka Yagneshwar, Suchi Kumari
Improving Online Algorithms via ML Predictions
Ravi Kumar, Manish Purohit, Zoya Svitkina