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
Common pitfalls to avoid while using multiobjective optimization in machine learning
Junaid Akhter, Paul David Fährmann, Konstantin Sonntag, Sebastian Peitz
Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey
Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, Shi Li, Jialei Li, Prashasti Sahu, Andre Richter, Tobias Kaefer, Michael Faerber
Obtaining physical layer data of latest generation networks for investigating adversary attacks
M. V. Ushakova, Yu. A. Ushakov, L. V. Legashev
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee, Scott Klasky, Anand Rangarajan, Sanjay Ranka
A Comprehensive Approach to Carbon Dioxide Emission Analysis in High Human Development Index Countries using Statistical and Machine Learning Techniques
Hamed Khosravi, Ahmed Shoyeb Raihan, Farzana Islam, Ashish Nimbarte, Imtiaz Ahmed
HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond
Stefan Abi-Karam, Rishov Sarkar, Allison Seigler, Sean Lowe, Zhigang Wei, Hanqiu Chen, Nanditha Rao, Lizy John, Aman Arora, Cong Hao
Artificial Intelligence in Bone Metastasis Analysis: Current Advancements, Opportunities and Challenges
Marwa Afnouch, Fares Bougourzi, Olfa Gaddour, Fadi Dornaika, Abdelmalik Taleb-Ahmed
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