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

January 20, 2023

Fair Credit Scorer through Bayesian Approach
Zhuo Zhao
Machine Learning Machine Learning Model Bayesian Approach Credit Scoring Fair Model Fair Predictor Fair Credit

January 19, 2023

SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify
Ioannis Dimolitsas, Spyridon Kantarelis, Afroditi Fouka
Machine Learning Model Study Feature Financial Success

January 17, 2023

January 13, 2023

MLOps: A Primer for Policymakers on a New Frontier in Machine Learning
Jazmia Henry
Machine Learning Machine Learning Model New Frontier MLOps System Public Policy Machine Learning Practitioner

January 12, 2023

Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks
Fadhel Ayed, Antonio De Domenico, Adrian Garcia-Rodriguez, David Lopez-Perez
Artificial Intelligence Machine Learning Model Next Generation Network Smart Infrastructure

January 11, 2023

The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features
Joris Pries, Guus Berkelmans, Sandjai Bhulai, Rob van der Mei
Machine Learning Model Human Prediction Inherent Interpretability Feature Wise Feature Importance

January 10, 2023

Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems
Junming Cao, Bihuan Chen, Longjie Hu, Jie Gao, Kaifeng Huang, Xin Peng
Machine Learning Machine Learning Model Global Impact System Description Dialogue System Complexity Matter Level Test Machine Learning Component

January 9, 2023

Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models
Antoine J. -P. Tixier, Matthew R. Hallowell
Machine Learning Model Generic Model Accident Datasets Specific Model Safety Prediction Task

January 8, 2023

Machine Learning Applied to Peruvian Vegetables Imports
Hugo Ticona-Salluca, Fred Torres-Cruz, Ernesto Nayer Tumi-Figueroa
Machine Learning Machine Learning Model LSTM Network Predictive Model Custom Fraud

January 4, 2023

Federated Learning for Data Streams
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni, Richard Vidal
Machine Learning Model Data Stream FL Algorithm Machine Learning Assumption

January 3, 2023

A Theory of I/O-Efficient Sparse Neural Network Inference
Niels Gleinig, Tal Ben-Nun, Torsten Hoefler
Machine Learning Model Theoretical Understanding Hardware Design Neural Network Inference Sparsity Pattern High Bandwidth Memory

December 30, 2022

ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models
Arkadipta De, Satya Swaroop Gudipudi, Sourab Panchanan, Maunendra Sankar Desarkar
Machine Learning Artificial Intelligence Machine Learning Model Unified Framework Theoretical Understanding AI System Black Box Machine Learning Model Responsible Artificial Intelligence Multi Dimensional Evaluation

December 29, 2022

Condensed Representation of Machine Learning Data
Rahman Salim Zengin, Volkan Sezer
Machine Learning Machine Learning Model K Mean Compact Representation State of the Art Clustering

December 23, 2022

Predicting Survival of Tongue Cancer Patients by Machine Learning Models
Angelos Vasilopoulos, Nan Miles Xi
Machine Learning Model Survival Prediction Oral Cancer

December 22, 2022

December 21, 2022

Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing
Chao Min, Guoquan Wen, Liangjie Gou, Xiaogang Li, Zhaozhong Yang
Machine Learning Model Inherent Interpretability Causal Discovery Causal Graph Latent Causal Oil Well

Machine Learning Model

Papers

Fair Credit Scorer through Bayesian Approach

SpotHitPy: A Study For ML-Based Song Hit Prediction Using Spotify

Command Line Interface Risk Modeling

Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models

MLOps: A Primer for Policymakers on a New Frontier in Machine Learning

Accordion: A Communication-Aware Machine Learning Framework for Next Generation Networks

The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features

Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems

Safer Together: Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than Company-Specific Models

Machine Learning Applied to Peruvian Vegetables Imports

Federated Learning for Data Streams

A Theory of I/O-Efficient Sparse Neural Network Inference

ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models

Condensed Representation of Machine Learning Data

Predicting Survival of Tongue Cancer Patients by Machine Learning Models

A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses

Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Federated Learning -- Methods, Applications and beyond

Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing