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

March 13, 2024

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Na Li, Chunyi Zhou, Yansong Gao, Hui Chen, Anmin Fu, Zhi Zhang, Yu Shui
Machine Learning Machine Learning Model Technical Challenge Financial Application Machine Unlearning Comprehensive Taxonomy Metric Library Personal Information Approximate Unlearning

March 12, 2024

XpertAI: uncovering model strategies for sub-manifolds
Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon
Machine Learning Model Explainable AI Class Manifold XAI Solution XAI Attribution

March 10, 2024

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI
Maria Teresa Parreira, Sukruth Gowdru Lingaraju, Adolfo Ramirez-Aristizabal, Manaswi Saha, Michael Kuniavsky, Wendy Ju
Deep Learning Model Machine Learning Model Study Feature Domain Generalization Model Robustness Human Reaction Failure Detection VAM HRI Interface

March 8, 2024

Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
Anupam Chaudhuri, Anj Simmons, Mohamed Abdelrazek
Generative Adversarial Network Machine Learning Model GAN Model Topological Feature Class Manifold Real Data Manifold Similarity

March 7, 2024

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Joonyoung F. Joung, Mun Hong Fong, Jihye Roh, Zhengkai Tu, John Bradshaw, Connor W. Coley
Machine Learning Model Chemical Reaction Reaction Path

March 4, 2024

February 28, 2024

Accelerating Computer Architecture Simulation through Machine Learning
Wajid Ali, Ayaz Akram
Machine Learning Machine Learning Model

February 27, 2024

Adversarial Perturbations of Physical Signals
Robert L. Bassett, Austin Van Dellen, Anthony P. Austin
Machine Learning Model Adversarial Perturbation Abnormal Signal Catastrophic Interference Signal Classification

February 25, 2024

An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan
Machine Learning Machine Learning Model Technical Challenge Empirical Study Portfolio Management Machine Learning Project

February 24, 2024

February 23, 2024

Statistical Agnostic Regression: a machine learning method to validate regression models
Juan M Gorriz, J. Ramirez, F. Segovia, F. J. Martinez-Murcia, C. Jiménez-Mesa, J. Suckling
Machine Learning Model New Machine Practical Method Linear Regression Regression Model Agnostic Learning Regression Problem

February 22, 2024

February 21, 2024

Stable Update of Regression Trees
Morten Blørstad, Berent Å. S. Lunde, Nello Blaser
Machine Learning Machine Learning Model Regression Tree Stable Code

February 20, 2024

Machine Learning Model

Papers

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

XpertAI: uncovering model strategies for sub-manifolds

A Study on Domain Generalization for Failure Detection through Human Reactions in HRI

Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset

Model Lakes

Predicting Learning Performance with Large Language Models: A Study in Adult Literacy

Accelerating Computer Architecture Simulation through Machine Learning

Adversarial Perturbations of Physical Signals

An Empirical Study of Challenges in Machine Learning Asset Management

ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices

Universal Model in Online Customer Service

Statistical Agnostic Regression: a machine learning method to validate regression models

Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data

Incorporating Expert Rules into Neural Networks in the Framework of Concept-Based Learning

Dependable Distributed Training of Compressed Machine Learning Models

Diversity-Aware Ensembling of Language Models Based on Topological Data Analysis

Stable Update of Regression Trees

Harnessing Large Language Models as Post-hoc Correctors

Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models