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
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
Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
Karen Roberts-Licklider, Theodore Trafalis
Machine Learning Applied to the Detection of Mycotoxin in Food: A Review
Alan Inglis, Andrew Parnell, Natarajan Subramani, Fiona Doohan
Explainable LightGBM Approach for Predicting Myocardial Infarction Mortality
Ana Letícia Garcez Vicente, Roseval Donisete Malaquias Junior, Roseli A. F. Romero
Symbolic Integration Algorithm Selection with Machine Learning: LSTMs vs Tree LSTMs
Rashid Barket, Matthew England, Jürgen Gerhard
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
Phoebe Jing, Yijing Gao, Xianlong Zeng
AI and Machine Learning for Next Generation Science Assessments
Xiaoming Zhai
Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
Matthew Colwell, Mahdi Abolghasemi
Machine Learning Techniques for MRI Data Processing at Expanding Scale
Taro Langner
Distributed Learning for Wi-Fi AP Load Prediction
Dariush Salami, Francesc Wilhelmi, Lorenzo Galati-Giordano, Mika Kasslin
PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
Henrik Folz, Joshua Henjes, Annika Heuer, Joscha Lahl, Philipp Olfert, Bjarne Seen, Sebastian Stabenau, Kai Krycki, Markus Lange-Hegermann, Helmand Shayan
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization
Bailey J. Eccles, Leon Wong, Blesson Varghese
Differential contributions of machine learning and statistical analysis to language and cognitive sciences
Kun Sun, Rong Wang
A survey of air combat behavior modeling using machine learning
Patrick Ribu Gorton, Andreas Strand, Karsten Brathen