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Found 9 papers, 8 papers with code
Graph Neural Rough Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
GREAD: Graph Neural Reaction-Diffusion Networks
In particular, diffusion equations have been widely used for designing the core processing layer of GNNs, and therefore they are inevitably vulnerable to the notorious oversmoothing problem.
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i. e. time series are under temporal drifts).
Blurring-Sharpening Process Models for Collaborative Filtering
Various methods have been proposed for collaborative filtering, ranging from matrix factorization to graph convolutional methods.
Ranked #1 on
Collaborative Filtering
on Gowalla
Prediction-based One-shot Dynamic Parking Pricing
Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.
Graph Neural Controlled Differential Equations for Traffic Forecasting
A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing.
Ranked #1 on
Traffic Prediction
on PeMSD7(L)
Linear, or Non-Linear, That is the Question!
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.
Climate Modeling with Neural Diffusion Equations
On the other hand, neural ordinary differential equations (NODEs) are to learn a latent governing equation of ODE from data.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF).
Ranked #1 on
Recommendation Systems
on Amazon-book
