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

January 25, 2024

Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks
Shuai Han, Mehdi Dastani, Shihan Wang
Reinforcement Learning Machine Learning Reward Function Sparse Reward Environment Sample Efficient Reinforcement Learning Reward Structure
Convolutional Neural Networks can achieve binary bail judgement classification
Amit Barman, Devangan Roy, Debapriya Paul, Indranil Dutta, Shouvik Kumar Guha, Samir Karmakar, Sudip Kumar Naskar
Machine Learning Convolutional Neural Network Convolutional Neural Network Model Indian Legal
Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru
Tanmay Ghosh, Nithin Nagaraj
Machine Learning Social Determinant Random Forest Model Discrete Choice Mode Choice

January 24, 2024

Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
Andrea Apicella, Francesco Isgrò, Roberto Prevete
Machine Learning Transfer Learning Data Leakage
Symbolic Equation Solving via Reinforcement Learning
Lennart Dabelow, Masahito Ueda
Reinforcement Learning Machine Learning Deep Neural Network Computer Algebra Local Transformation
Lessons on Datasets and Paradigms in Machine Learning for Symbolic Computation: A Case Study on CAD
Tereso del Río, Matthew England
Machine Learning Data Set Machine Learning Model Symbolic Computation Explanatory Paradigm Variable Ordering
Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques
V Mato-Abad, A Labiano-Fontcuberta, S Rodriguez-Yanez, R Garcia-Vazquez, CR Munteanu, J Andrade-Garda, A Domingo-Santos, V Galan Sanchez-Seco, Y Aladro, ML Martinez-Gines, L Ayuso, J Benito-Leon
Machine Learning Classification Code Multiple Sclerosis Clinical Language Subcortical Segmentation
Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework
Xia Chen, Alexander Rex, Janis Woelke, Christoph Eckert, Boris Bensmann, Richard Hanke-Rauschenbach, Philipp Geyer
Machine Learning Different PaRT Data Driven Model
Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques
Tejas Vyas, Mohsena Chowdhury, Xiaojiao Xiao, Mathias Claeys, Géraldine Ong, Guanghui Wang
Deep Learning Machine Learning Mitral Valve

January 22, 2024

Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui, Amine Abouaomar, Abdellatif Kobbane, Mohamed El Kamili
Machine Learning Federated Learning Internet of Thing Communication Efficient Efficient Collaboration Coalition Game Device Model
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
Leonardo Castro-Gonzalez, Yi-Ling Chung, Hannak Rose Kirk, John Francis, Angus R. Williams, Pica Johansson, Jonathan Bright
Large Language Model Language Model Machine Learning Transfer Learning System Performance Weak Supervision Computational Social Science Zero Shot Prompting Frugal Learning
Expert-Driven Monitoring of Operational ML Models
Joran Leest, Claudia Raibulet, Ilias Gerostathopoulos, Patricia Lago
Machine Learning Concept Drift Expert Driven Monitoring Domain Expertise Human Oversight
Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing
Rrubaa Panchendrarajan, Arkaitz Zubiaga
Machine Learning Natural Language Processing Timely Survey Language Understanding Hybrid Approach Symbolic Approach
An Exploratory Study of Multimodal Physiological Data in Jazz Improvisation Using Basic Machine Learning Techniques
Yawen Zhang
Machine Learning Physiological Signal Exploratory Study Jazz Improvisation Music Sequence
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Chao Liu, Boxi Chen, Wei Shao, Chris Zhang, Kelvin Wong, Yi Zhang
Machine Learning Timely Survey Machine Learning Model New Machine Open Source Library Unraveling Key Factor IoT Device Identification

January 21, 2024

Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
Machine Learning Loss Function Great Truth Makeup Product Image
Frost Prediction Using Machine Learning Methods in Fars Province
Milad Barooni, Koorush Ziarati, Ali Barooni
Deep Learning Machine Learning Temporal Convolutional Network Gradient Boosting Temperature Measurement Time Prediction Frost Prediction

Machine Learning

Papers

Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks

Convolutional Neural Networks can achieve binary bail judgement classification

Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru

Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning

Symbolic Equation Solving via Reinforcement Learning

Lessons on Datasets and Paradigms in Machine Learning for Symbolic Computation: A Case Study on CAD

Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques

Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework

Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques

Gas trap prediction from 3D seismic and well test data using machine learning

LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method

DALex: Lexicase-like Selection via Diverse Aggregation

Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems

Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data

Expert-Driven Monitoring of Operational ML Models

Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing

An Exploratory Study of Multimodal Physiological Data in Jazz Improvisation Using Basic Machine Learning Techniques

Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them

Exploring the Truth and Beauty of Theory Landscapes with Machine Learning

Frost Prediction Using Machine Learning Methods in Fars Province