Summary of Adafgl: a New Paradigm For Federated Node Classification with Topology Heterogeneity, by Xunkai Li et al.
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneityby Xunkai Li, Zhengyu Wu,…
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneityby Xunkai Li, Zhengyu Wu,…
FedGTA: Topology-aware Averaging for Federated Graph Learningby Xunkai Li, Zhengyu Wu, Wentao Zhang, Yinlin Zhu,…
Towards Effective and General Graph Unlearning via Mutual Evolutionby Xunkai Li, Yulin Zhao, Zhengyu Wu,…
Sequential Model for Predicting Patient Adherence in Subcutaneous Immunotherapy for Allergic Rhinitisby Yin Li, Yu…
Open the Black Box: Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learningby Ge Li, Hongyi…
Frost Prediction Using Machine Learning Methods in Fars Provinceby Milad Barooni, Koorush Ziarati, Ali BarooniFirst…
CaBuAr: California Burned Areas dataset for delineationby Daniele Rege Cambrin, Luca Colomba, Paolo GarzaFirst submitted…
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?by Siddharth Mansingh, Michal Kucer, Garrett Kenyon,…
Information-Theoretic State Variable Selection for Reinforcement Learningby Charles Westphal, Stephen Hailes, Mirco MusolesiFirst submitted to…
Enhancing selectivity using Wasserstein distance based reweighingby Pratik WorahFirst submitted to arxiv on: 21 Jan…