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 27, 2023

Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
Machine Learning Model Procedural Fairness Bayesian Optimization Task Utility Pareto Optimal Fair Decision Automatic Discovery

November 26, 2023

Confidence Is All You Need for MI Attacks
Abhishek Sinha, Himanshi Tibrewal, Mansi Gupta, Nikhar Waghela, Shivank Garg
Training Data Machine Learning Model Membership Inference Attack Sensitive Data High Confidence Label Attack MI Attack

November 24, 2023

Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China
Jen-Yin Yeh, Hsin-Yu Chiu, Jhih-Huei Huang
Machine Learning Machine Learning Model Case Relevance Rural China Peer to Peer Software Failure

November 23, 2023

November 22, 2023

November 21, 2023

Continuous Management of Machine Learning-Based Application Behavior
Marco Anisetti, Claudio A. Ardagna, Nicola Bena, Ernesto Damiani, Paolo G. Panero
Machine Learning Machine Learning Model Multiple Model

November 20, 2023

Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance
Bappaditya Dey, Anh Tuan Ngo, Sara Sacchi, Victor Blanco, Philippe Leray, Sandip Halder
Machine Learning Model Smart Device Semiconductor Manufacturing Capacitive Sensing Extreme Ultraviolet Lithography Model Metrology Semiconductor Wafer

November 19, 2023

Negotiated Representations for Machine Mearning Application
Nuri Korhan, Samet Bayram
Machine Learning Model Continual LEArning Model Overfitting Regularization Technique Fair Representation

November 17, 2023

Machine Learning Model

Papers

Automated discovery of trade-off between utility, privacy and fairness in machine learning models

Confidence Is All You Need for MI Attacks

Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China

Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects

Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences

On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study

MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis

A Survey of Serverless Machine Learning Model Inference

Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia

Analyzing the Evolution and Maintenance of ML Models on Hugging Face

Continuous Management of Machine Learning-Based Application Behavior

Applying Machine Learning Models on Metrology Data for Predicting Device Electrical Performance

Negotiated Representations for Machine Mearning Application

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations

A novel post-hoc explanation comparison metric and applications

Active Inference on the Edge: A Design Study

The Next 700 ML-Enabled Compiler Optimizations

Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector

Quantum-Assisted Simulation: A Framework for Designing Machine Learning Models in the Quantum Computing Domain

How False Data Affects Machine Learning Models in Electrochemistry?