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Found 5 papers, 1 papers with code
Stochastic Graph Recurrent Neural Network
In addition, to alleviate KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence.
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective
Based on our theoretical and empirical analysis, we establish a universal theoretical framework of GCN from an optimization perspective and derive a novel convolutional kernel named GCN+ which has lower parameter amount while relieving the over-smoothing inherently.
ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters.
AdaL: Adaptive Gradient Transformation Contributes to Convergences and Generalizations
Inspired by this, we propose AdaL, with a transformation on the original gradient.
TFDPM: Attack detection for cyber-physical systems with diffusion probabilistic models
With the development of AIoT, data-driven attack detection methods for cyber-physical systems (CPSs) have attracted lots of attention.
