Machine Learning Model

Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.

Papers

September 21, 2022

September 20, 2022

FACT: Learning Governing Abstractions Behind Integer Sequences
Peter Belcák, Ard Kastrati, Flavio Schenker, Roger Wattenhofer
Machine Learning Model Knowledge Representation Metric Aware Abstraction Random Sequence Multi FAct

September 19, 2022

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions
Swati Tyagi
Machine Learning Machine Learning Model Explainable AI Credit Scoring Investment Decision

September 17, 2022

A review of predictive uncertainty estimation with machine learning
Hristos Tyralis, Georgia Papacharalampous
Machine Learning Machine Learning Model Narrative Review Machine Learning Algorithm Predictive Uncertainty

September 16, 2022

IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
Li Yang, Abdallah Shami
Machine Learning Machine Learning Model Dynamic Environment

September 15, 2022

September 13, 2022

Detection of Malicious Websites Using Machine Learning Techniques
Adebayo Oshingbesan, Courage Ekoh, Chukwuemeka Okobi, Aime Munezero, Kagame Richard
Machine Learning Machine Learning Model Data Detection Random Forest Malicious Domain

September 12, 2022

September 10, 2022

Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder
Federico Sabbatini, Catia Grimani
Machine Learning Model Human Prediction Knowledge Extraction Symbolic Knowledge Opaque Machine Learning Ensemble Prediction Cosmic Ray

September 6, 2022

September 4, 2022

A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset
Toluwalase A. Olukoga, Yin Feng
Machine Learning Machine Learning Model Classification Code Ensemble Learning Drilling Data

Machine Learning Model

Papers

Leak Detection in Natural Gas Pipeline Using Machine Learning Models

Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring

FACT: Learning Governing Abstractions Behind Integer Sequences

Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions

A review of predictive uncertainty estimation with machine learning

IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective

Avoiding Biased Clinical Machine Learning Model Performance Estimates in the Presence of Label Selection

Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP

A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems

MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat

Metrics to guide development of machine learning algorithms for malaria diagnosis

Data Privacy and Trustworthy Machine Learning

An ensemble Multi-Agent System for non-linear classification

Detection of Malicious Websites Using Machine Learning Techniques

Model interpretation using improved local regression with variable importance

Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy

Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder

Explaining Machine Learning Models in Natural Conversations: Towards a Conversational XAI Agent

A Survey of Machine Unlearning

A Case Study on the Classification of Lost Circulation Events During Drilling using Machine Learning Techniques on an Imbalanced Large Dataset