Ars Technica

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...
IEEE

Optimal Channel Selection for FY-4B GIIRS Explainable Machine Learning Cloud Detection Algorithm

Abstract: Cloud detection is a crucial preliminary step for assimilating meteorological satellite observation and retrieving other atmospheric parameters. This article presents an explainable machine ...
Frontiers

Explainable machine learning to predict the cost of capital

This study investigates the impact of financial and non-financial factors on a firm's ex-ante cost of capital, which is the reflection of investors' perception on a firm's riskiness. Departing from ...
IEEE

Enhancing Credit Risk Decision-Making in Supply Chain Finance With Interpretable Machine Learning Model

Abstract: The increasing complexity of supply chain finance poses significant challenges to effective credit risk assessment. Traditional black-box models often fail to provide insights into the ...
Scientific Research Publishing

Research on Telecom Customer Churn Prediction Based on GA-XGBoost and SHAP ()

To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN ...
Frontiers

Interaction Analysis Based on Shapley Values and Extreme Gradient Boosting: A Realistic Simulation and Application to a Large Epidemiological Prospective Study

SHapley Additive exPlanations (SHAP) based on tree-based machine learning methods have been proposed to interpret interactions between exposures in observational studies, but their performance in ...