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Found 8 papers, 2 papers with code
Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network
This performance relies heavily on the configuration of the network parameters.
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
To make practical, we further devise an offline RL algorithm to approximately find the solution.
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Node Copying: A Random Graph Model for Effective Graph Sampling
In this work, we introduce the node copying model for constructing a distribution over graphs.
Universality of parametric Coupling Flows over parametric diffeomorphisms
Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.
Sparse Personalized Federated Learning
By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.
Structured Directional Pruning via Perturbation Orthogonal Projection
Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss.
A Causal Direction Test for Heterogeneous Populations
Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data.
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model.
