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Found 6 papers, 1 papers with code
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
Our approach addresses a critical need in contemporary machine learning applications by providing unprecedented insights into the inner workings of complex models, fostering trust, transparency, and accountability in their deployment across diverse domains.
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
In the realm of cryptocurrency, the prediction of Bitcoin prices has garnered substantial attention due to its potential impact on financial markets and investment strategies.
ADA-YOLO: Dynamic Fusion of YOLOv8 and Adaptive Heads for Precise Image Detection and Diagnosis
To address this issue, we propose ADA-YOLO, a light-weight yet effective method for medical object detection that integrates attention-based mechanisms with the YOLOv8 architecture.
Decision-making of Emergent Incident based on P-MADDPG
In this paper, we propose a P-MADDPG algorithm to solve the emergency decision-making problem of emergent incidents, which predicts the nodes where an incident may occur in the next time by GRU model and makes decisions before the incident occurs, thus solving the problem that the decision speed cannot keep up with the spreading speed.
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
Distributed Weighted Least Squares Estimator Based on ADMM
To this end, we first exploit the equivalent relation between the information filter and WLS estimator.
