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
Whither Bias Goes, I Will Go: An Integrative, Systematic Review of Algorithmic Bias Mitigation
Louis Hickman, Christopher Huynh, Jessica Gass, Brandon Booth, Jason Kuruzovich, Louis Tay
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
Jintao Ren, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Silin Chen, Ming Li, Jiawei Xu, Ming Liu
Enhancing AI Accessibility in Veterinary Medicine: Linking Classifiers and Electronic Health Records
Chun Yin Kong, Picasso Vasquez, Makan Farhoodimoghadam, Chris Brandt, Titus C. Brown, Krystle L. Reagan, Allison Zwingenberger, Stefan M. Keller
Machine Learning Aided Modeling of Granular Materials: A Review
Mengqi Wang, Krishna Kumar, Y. T. Feng, Tongming Qu, Min Wang
Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions
Jannatun Nayeem Pinky, Ramya Akula
Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero
An explainable machine learning approach for energy forecasting at the household level
Pauline Béraud, Margaux Rioux, Michel Babany, Philippe de La Chevasnerie, Damien Theis, Giacomo Teodori, Chloé Pinguet, Romane Rigaud, François Leclerc
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
Vasileios Vatellis
Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data
Quang Dang, Murat Kucukosmanoglu, Michael Anoruo, Golshan Kargosha, Sarah Conklin, Justin Brooks
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation
Ho Sung Shim, Hyoungjun Park, Kyuhan Lee, Jang-Sun Park, Seonhye Kang
In-context learning and Occam's razor
Eric Elmoznino, Tom Marty, Tejas Kasetty, Leo Gagnon, Sarthak Mittal, Mahan Fathi, Dhanya Sridhar, Guillaume Lajoie
MACK: Mismodeling Addressed with Contrastive Knowledge
Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
Ernesto Arganda, Marcela Carena, Martín de los Rios, Andres D. Perez, Duncan Rocha, Rosa M. Sandá Seoane, Carlos E. M. Wagner
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Hagen Holthusen, Tim Brepols, Kevin Linka, Ellen Kuhl
Advancements In Heart Disease Prediction: A Machine Learning Approach For Early Detection And Risk Assessment
Balaji Shesharao Ingole, Vishnu Ramineni, Nikhil Bangad, Koushik Kumar Ganeeb, Priyankkumar Patel
SSET: Swapping-Sliding Explanation for Time Series Classifiers in Affect Detection
Nazanin Fouladgar, Marjan Alirezaie, Kary Främling
Machine learning approach to brain tumor detection and classification
Alice Oh, Inyoung Noh, Jian Choo, Jihoo Lee, Justin Park, Kate Hwang, Sanghyeon Kim, Soo Min Oh