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Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
Enforcing orthonormal or isometric property for the weight matrices has been shown to enhance the training of deep neural networks by mitigating gradient exploding/vanishing and increasing the robustness of the learned networks.
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning
Instead of "opening set", i. e., modeling OOD distribution, Prototype Fission "closes set" and makes it hard for OOD samples to fit in sub-class latent space.
Feature Importance-aware Graph Attention Network and Dueling Double Deep Q-Network Combined Approach for Critical Node Detection Problems
Extensive comparison results are given to show its effectiveness in solving CNP.
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