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
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
Jingwei Zuo, George Arvanitakis, Mthandazo Ndhlovu, Hakim Hacid
Improving Pallet Detection Using Synthetic Data
Henry Gann, Josiah Bull, Trevor Gee, Mahla Nejati
The Relevance Feature and Vector Machine for health applications
Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Emilio Parrado-Hernández, Vanessa Gómez-Verdejo
Comparison of machine learning and statistical approaches for digital elevation model (DEM) correction: interim results
Chukwuma Okolie, Adedayo Adeleke, Julian Smit, Jon Mills, Iyke Maduako, Caleb Ogbeta
Investigating Reproducibility in Deep Learning-Based Software Fault Prediction
Adil Mukhtar, Dietmar Jannach, Franz Wotawa
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks
Ben Fauber
Machine learning applied to omics data
Aida Calviño, Almudena Moreno-Ribera, Silvia Pineda
Comparison of edge computing methods in Internet of Things architectures for efficient estimation of indoor environmental parameters with Machine Learning
Jose-Carlos Gamazo-Real, Raul Torres Fernandez, Adrian Murillo Armas
Can machine learning predict citizen-reported angler behavior?
Julia S. Schmid, Sean Simmons, Mark A. Lewis, Mark S. Poesch, Pouria Ramazi
ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied to Vision Transformers
Ikhyun Cho, Changyeon Park, Julia Hockenmaier
IoT Network Traffic Analysis with Deep Learning
Mei Liu, Leon Yang
NeRCC: Nested-Regression Coded Computing for Resilient Distributed Prediction Serving Systems
Parsa Moradi, Mohammad Ali Maddah-Ali
Scaling laws for learning with real and surrogate data
Ayush Jain, Andrea Montanari, Eren Sasoglu
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Yvonne Zhou, Mingyu Liang, Ivan Brugere, Dana Dachman-Soled, Danial Dervovic, Antigoni Polychroniadou, Min Wu
Tempered Calculus for ML: Application to Hyperbolic Model Embedding
Richard Nock, Ehsan Amid, Frank Nielsen, Alexander Soen, Manfred K. Warmuth
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol
Hector Alaiz-Moreton, Jose Aveleira-Mata, Jorge Ondicol-Garcia, Angel Luis Muñoz-Castañeda, Isaías García, Carmen Benavides
Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in AI, Machine Learning, and Digital Twin
Md Muzakkir Quamar, Ali Nasir