Summary of Biases in Expected Goals Models Confound Finishing Ability, by Jesse Davis and Pieter Robberechts
Biases in Expected Goals Models Confound Finishing Abilityby Jesse Davis, Pieter RobberechtsFirst submitted to arxiv…
Biases in Expected Goals Models Confound Finishing Abilityby Jesse Davis, Pieter RobberechtsFirst submitted to arxiv…
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregationby Ruizhe Zhang, Xinke Jiang,…
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAVby Florian Achermann, Thomas…
HGAttack: Transferable Heterogeneous Graph Adversarial Attackby He Zhao, Zhiwei Zeng, Yongwei Wang, Deheng Ye, Chunyan…
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compressionby Ho Fung Tsoi, Vladimir Loncar,…
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classificationby Yutong Xia,…
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screeningby Taulant Koka, Jasin Machkour,…
Developing an AI-based Integrated System for Bee Health Evaluationby Andrew LiangFirst submitted to arxiv on:…
Optimizing Medication Decisions for Patients with Atrial Fibrillation through Path Development Networkby Tian XieFirst submitted…
Improving Local Training in Federated Learning via Temperature Scalingby Kichang Lee, Songkuk Kim, JeongGil KoFirst…