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
Comparative Analysis of Machine Learning and Deep Learning Models for Classifying Squamous Epithelial Cells of the Cervix
Subhasish Das, Satish K Panda, Madhusmita Sethy, Prajna Paramita Giri, Ashwini K Nanda
SoK: A Systems Perspective on Compound AI Threats and Countermeasures
Sarbartha Banerjee, Prateek Sahu, Mulong Luo, Anjo Vahldiek-Oberwagner, Neeraja J. Yadwadkar, Mohit Tiwari
Executable QR codes with Machine Learning for Industrial Applications
Stefano Scanzio, Francesco Velluto, Matteo Rosani, Lukasz Wisniewski, Gianluca Cena
Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization
Mohammad R. Salmanpour, Morteza Alizadeh, Ghazal Mousavi, Saba Sadeghi, Sajad Amiri, Mehrdad Oveisi, Arman Rahmim, Ilker Hacihaliloglu
Revitalizing Electoral Trust: Enhancing Transparency and Efficiency through Automated Voter Counting with Machine Learning
Mir Faris, Syeda Aynul Karim, Md. Juniadul Islam
Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition
Haoyi Jia, Abhilasha Dave, Julia Gonski, Ryan Herbst
Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods
Baljinnyam Dayan
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and Classification
Nathaniel Hanson, Philip Manke, Simon Birkholz, Maximilian Mühlbauer, Rene Heine, Arnd Brandes
A Review on Machine Unlearning
Haibo Zhang, Toru Nakamura, Takamasa Isohara, Kouichi Sakurai
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction
Christoph Brosch, Alexander Bouwens, Sebastian Bast, Swen Haab, Rolf Krieger
On the Cost of Model-Serving Frameworks: An Experimental Evaluation
Pasquale De Rosa, Yérom-David Bromberg, Pascal Felber, Djob Mvondo, Valerio Schiavoni
Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques
Maliheh Alaeddini
Unveiling Topological Structures in Text: A Comprehensive Survey of Topological Data Analysis Applications in NLP
Adaku Uchendu, Thai Le
Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications
Laurent Schmalen, Vincent Lauinger, Jonas Ney, Norbert Wehn, Patrick Matalla, Sebastian Randel, Alexander von Bank, Eike-Manuel Edelmann
Physics-informed Machine Learning for Battery Pack Thermal Management
Zheng Liu, Yuan Jiang, Yumeng Li, Pingfeng Wang
Fair Secretaries with Unfair Predictions
Eric Balkanski, Will Ma, Andreas Maggiori