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

November 21, 2022

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
Alice E. A. Allen, Alexandre Tkatchenko
Machine Learning Model Data Driven Nonlinear Model Regression Model Interdisciplinary Perspective Natural Science Complex Predictive Model Interpretable Regression

November 19, 2022

November 18, 2022

Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction
Montgomery Flora, Corey Potvin, Amy McGovern, Shawn Handler
Machine Learning Machine Learning Model Dimensionality Reduction Large Relevance Improvement Feature Importance Model Explanation Explainability Method Explanation Method Explanation Faithfulness

November 16, 2022

Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement
Montgomery Flora, Corey Potvin, Amy McGovern, Shawn Handler
Machine Learning Machine Learning Model High Explainability Explanation Method Current Method Global Explanation Opinion Disagreement

November 15, 2022

November 14, 2022

November 11, 2022

November 10, 2022

Reconstruction and analysis of negatively buoyant jets with interpretable machine learning
Marta Alvir, Luka Grbčić, Ante Sikirica, Lado Kranjčević
Machine Learning Model General Analysis Full State Reconstruction Interpretable Machine Learning Support Vector Regression Multiple Jet

November 9, 2022

Accountable and Explainable Methods for Complex Reasoning over Text
Pepa Atanasova
Machine Learning Adversarial Attack Machine Learning Model Text Modality Complex Reasoning Transparency Index Explanation Method

November 8, 2022

Hyperparameter optimization in deep multi-target prediction
Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman
Machine Learning Machine Learning Model Hyperparameter Optimization Online autoML AutoML Framework Multi Target Auto Sklearn

November 7, 2022

FedGrad: Optimisation in Decentralised Machine Learning
Mann Patel
Machine Learning Model Computation Method Optimization Process Decentralized Machine Learning Single Model

November 6, 2022

Prediction of superconducting properties of materials based on machine learning models
Jie Hu, Yongquan Jiang, Yang Yan, Houchen Zuo
Machine Learning Machine Learning Model Human Prediction Many Property Material Response XGBoost Model Material SCIence Deep Regression Forest

November 4, 2022