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

February 10, 2022

Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
Luis Pedro Silvestrin, Harry van Zanten, Mark Hoogendoorn, Ger Koole
Data Set Machine Learning Model General Analysis Practical Algorithm Least Square Hyperparameter Tuning Transfer Learning Algorithm Input Dimension

February 9, 2022

MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis
Haodi Zhang, Chenyu Xu, Peirou Liang, Ke Duan, Hao Ren, Weibin Cheng, Kaishun Wu
Machine Learning Model Inherent Interpretability Domain Knowledge Multimodal Phenomenon Diagnosis Prediction Multimodal Diagnosis

February 7, 2022

February 6, 2022

Machine Learning Models in Stock Market Prediction
Gurjeet Singh
Machine Learning Model Random Forest Support Vector Machine Prediction Performance Indian Stock Market Stock Market Prediction Trading Data

February 5, 2022

February 4, 2022

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
Patrick Link, Miltiadis Poursanidis, Jochen Schmid, Rebekka Zache, Martin von Kurnatowski, Uwe Teicher, Steffen Ihlenfeldt
Machine Learning Machine Learning Model Expert Knowledge Traditional Machine Learning Quality Prediction Capturing Maneuver

February 3, 2022

February 2, 2022

February 1, 2022

January 31, 2022

Comparative Study of Machine Learning Models for Stock Price Prediction
Ogulcan E. Orsel, Sasha S. Yamada
Machine Learning Model Long Short Term Memory Comparative Study Stock Price Prediction Market Volatility Stock Market Data

Machine Learning Model

Papers

Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case

MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis

On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps

Optimal Ratio for Data Splitting

Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning

Nonparametric Uncertainty Quantification for Single Deterministic Neural Network

Jury Learning: Integrating Dissenting Voices into Machine Learning Models

Machine Learning Models in Stock Market Prediction

A Game-theoretic Understanding of Repeated Explanations in ML Models

A Survey on Automated Sarcasm Detection on Twitter

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

Incorporating Sum Constraints into Multitask Gaussian Processes

Can machines solve general queueing systems?

Approximating Full Conformal Prediction at Scale via Influence Functions

Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees

Achieving Fairness at No Utility Cost via Data Reweighing with Influence

Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification

Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement

Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls

Comparative Study of Machine Learning Models for Stock Price Prediction