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
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Quang H. Nguyen, Duy C. Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V. Chawla, Khoa D. Doan
Physics-Informed Machine Learning for Smart Additive Manufacturing
Rahul Sharma, Maziar Raissi, Y. B. Guo
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
Mateus Guimarães Lima, Antony Carvalho, João Gabriel Álvares, Clayton Escouper das Chagas, Ronaldo Ribeiro Goldschmidt
Brain Tumor Classification From MRI Images Using Machine Learning
Vidhyapriya Ranganathan, Celshiya Udaiyar, Jaisree Jayanth, Meghaa P, Srija B, Uthra S
Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning
Daniel Geissler, Paul Lukowicz
MonoSparse-CAM: Harnessing Monotonicity and Sparsity for Enhanced Tree Model Processing on CAMs
Tergel Molom-Ochir, Brady Taylor, Hai Li, Yiran Chen
Real-time gravitational-wave inference for binary neutron stars using machine learning
Maximilian Dax, Stephen R. Green, Jonathan Gair, Nihar Gupte, Michael Pürrer, Vivien Raymond, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures
Sophia Sanborn, Johan Mathe, Mathilde Papillon, Domas Buracas, Hansen J Lillemark, Christian Shewmake, Abby Bertics, Xavier Pennec, Nina Miolane
Machine Learning in High Volume Media Manufacturing
Siddarth Reddy Karuka, Abhinav Sunderrajan, Zheng Zheng, Yong Woon Tiean, Ganesh Nagappan, Allan Luk
Improving Molecular Modeling with Geometric GNNs: an Empirical Study
Ali Ramlaoui, Théo Saulus, Basile Terver, Victor Schmidt, David Rolnick, Fragkiskos D. Malliaros, Alexandre Duval
SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories
Anand Babu
Position: Measure Dataset Diversity, Don't Just Claim It
Dora Zhao, Jerone T. A. Andrews, Orestis Papakyriakopoulos, Alice Xiang
Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data
Ritesh Mehta, Aleksandar Pramov, Shashank Verma
ICD Codes are Insufficient to Create Datasets for Machine Learning: An Evaluation Using All of Us Data for Coccidioidomycosis and Myocardial Infarction
Abigail E. Whitlock, Gondy Leroy, Fariba M. Donovan, John N. Galgiani
When to Accept Automated Predictions and When to Defer to Human Judgment?
Daniel Sikar, Artur Garcez, Tillman Weyde, Robin Bloomfield, Kaleem Peeroo
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
Parastoo Semnani, Mihail Bogojeski, Florian Bley, Zizheng Zhang, Qiong Wu, Thomas Kneib, Jan Herrmann, Christoph Weisser, Florina Patcas, Klaus-Robert Müller
Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans
Stefan Grafberger
CHILLI: A data context-aware perturbation method for XAI
Saif Anwar, Nathan Griffiths, Abhir Bhalerao, Thomas Popham