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Improving Single and Multi-Agent Deep Reinforcement Learning Methods - Thesis abstract

Improving Single and Multi-Agent Deep Reinforcement Learning Methods - Thesis abstract

Fake news detection by Machine Learning in Latin America - A Systematic Review

Fake news detection by Machine Learning in Latin America - A Systematic Review

Machine learning models to predict electricity consumption and the impacts of COVID-19 in Portugal - Thesis

Machine learning models to predict electricity consumption and the impacts of COVID-19 in Portugal - Thesis

Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers - Research report

Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers - Research report

Predicting Default Probability in Emerging Economies: A Comparative Machine Learning Approach in the Tunisian Banking Sector

Predicting Default Probability in Emerging Economies: A Comparative Machine Learning Approach in the Tunisian Banking Sector

THE EXPECTED VALUE OF APPLYING MACHINE LEARNING FOR PREDICTIVE LEAD SCORING BASED ON CUSTOMER CONTACT-FORM INPUT - A CASE IN THE B2B ENERGY SECTOR

THE EXPECTED VALUE OF APPLYING MACHINE LEARNING FOR PREDICTIVE LEAD SCORING BASED ON CUSTOMER CONTACT-FORM INPUT - A CASE IN THE B2B ENERGY SECTOR

Tail-Risk and Sustainability: Can ESG scores accurately predict Value at Risk? - A machine learning based approach

Tail-Risk and Sustainability: Can ESG scores accurately predict Value at Risk? - A machine learning based approach

Rapid Classiﬁcation of Petroleum Waxes - A Vis-NIR Spectroscopy and Machine Learning Approach

Rapid Classiﬁcation of Petroleum Waxes - A Vis-NIR Spectroscopy and Machine Learning Approach

Predicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods

Predicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods

Human-in-the-loop machine learning - A state of the art - Review and definitions

Human-in-the-loop machine learning - A state of the art - Review and definitions

A Large-Scale Study of Probabilistic Calibration in Neural Network Regression - Proceedings

A Large-Scale Study of Probabilistic Calibration in Neural Network Regression - Proceedings

Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes

Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes

Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models

Predicting chronic kidney disease progression using small pathology datasets and explainable machine learning models

Tensor Networks for Explainable Machine Learning in Cybersecurity - Research

Tensor Networks for Explainable Machine Learning in Cybersecurity - Research

LeOCLR - Leveraging Original Images for Contrastive Learning of Visual Representations - A framework for improved instance discrimination

LeOCLR - Leveraging Original Images for Contrastive Learning of Visual Representations - A framework for improved instance discrimination