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
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
Rafiullah Omar, Justus Bogner, Joran Leest, Vincenzo Stoico, Patricia Lago, Henry Muccini
A critical appraisal of water table depth estimation: Challenges and opportunities within machine learning
Joseph Janssen, Ardalan Tootchi, Ali A. Ameli
Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study
Maryam Allahbakhshi, Aylar Sadri, Seyed Omid Shahdi
Predicting Fairness of ML Software Configurations
Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich, Ashutosh Trivedi, Saeid Tizpaz-Niari
What is Reproducibility in Artificial Intelligence and Machine Learning Research?
Abhyuday Desai, Mohamed Abdelhamid, Nakul R. Padalkar
The Landscape of Unfolding with Machine Learning
Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Xi Xin, Giles Hooker, Fei Huang
Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Rieke Müller, Mohamed Abdelaal, Davor Stjelja
Machine Learning for Quantum Computing Specialists
Daniel Goldsmith, M M Hassan Mahmud
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research
Daniel Gibert
Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD
Valentina Zaccaria, Chiara Masiero, David Dandolo, Gian Antonio Susto
Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readout
Julia Gonski, Aseem Gupta, Haoyi Jia, Hyunjoon Kim, Lorenzo Rota, Larry Ruckman, Angelo Dragone, Ryan Herbst
Alice's Adventures in a Differentiable Wonderland -- Volume I, A Tour of the Land
Simone Scardapane
Machine Learning based prediction of Vanadium Redox Flow Battery temperature rise under different charge-discharge conditions
Anirudh Narayan D, Akshat Johar, Divye Kalra, Bhavya Ardeshna, Ankur Bhattacharjee
Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study
Chunlei Xu, Michael Pregesbauer, Naga Sravani Chilukuri, Daniel Windhager, Mahsa Yousefi, Pedro Julian, Lothar Ratschbacher
Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis
Alexandre Gemayel, Dimitrios Michael Manias, Abdallah Shami