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FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs
First, we conduct a theoretical analysis of dropout in GNNs using rademacher complexity and demonstrate that the generalization error of traditional random dropout methods is constrained by a function related to the dropout rate.
Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery
Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity and topological relationships of different body parts.
Functional Connectome: Approximating Brain Networks with Artificial Neural Networks
We show that trained deep neural networks are able to perform zero-shot generalisation in novel environments, and allows for a wealth of tasks such as decoding the animal's location in space with high accuracy.
