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
Aprendizado de m\'aquina aplicado na eletroqu\'imica
Carlos Eduardo do Egito Araújo, Lívia F. Sgobbi, Iwens Gervasio Sene, Sergio Teixeira de Carvalho
PartIR: Composing SPMD Partitioning Strategies for Machine Learning
Sami Alabed, Daniel Belov, Bart Chrzaszcz, Juliana Franco, Dominik Grewe, Dougal Maclaurin, James Molloy, Tom Natan, Tamara Norman, Xiaoyue Pan, Adam Paszke, Norman A. Rink, Michael Schaarschmidt, Timur Sitdikov, Agnieszka Swietlik, Dimitrios Vytiniotis, Joel Wee
Combining Cloud and Mobile Computing for Machine Learning
Ruiqi Xu, Tianchi Zhang
Unlocking Unlabeled Data: Ensemble Learning with the Hui- Walter Paradigm for Performance Estimation in Online and Static Settings
Kevin Slote, Elaine Lee
Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
Junhao Wen, Mathilde Antoniades, Zhijian Yang, Gyujoon Hwang, Ioanna Skampardoni, Rongguang Wang, Christos Davatzikos
Inductive Models for Artificial Intelligence Systems are Insufficient without Good Explanations
Udesh Habaraduwa
Improved Performances and Motivation in Intelligent Tutoring Systems: Combining Machine Learning and Learner Choice
Benjamin Clément, Hélène Sauzéon, Didier Roy, Pierre-Yves Oudeyer
Efficient and Mathematically Robust Operations for Certified Neural Networks Inference
Fabien Geyer, Johannes Freitag, Tobias Schulz, Sascha Uhrig
Machine Learning on Dynamic Graphs: A Survey on Applications
Sanaz Hasanzadeh Fard
Analyzing Brain Activity During Learning Tasks with EEG and Machine Learning
Ryan Cho, Mobasshira Zaman, Kyu Taek Cho, Jaejin Hwang
Machine Learning Techniques to Identify Hand Gestures amidst Forearm Muscle Signals
Ryan Cho, Sunil Patel, Kyu Taek Cho, Jaejin Hwang
Optimal Data Splitting in Distributed Optimization for Machine Learning
Daniil Medyakov, Gleb Molodtsov, Aleksandr Beznosikov, Alexander Gasnikov
Compute-Efficient Active Learning
Gábor Németh, Tamás Matuszka
A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning
Chunxu Cao, Qiang Zhang
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
William Watkins, Heehwan Wang, Sangyoon Bae, Huan-Hsin Tseng, Jiook Cha, Samuel Yen-Chi Chen, Shinjae Yoo