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Found 5 papers, 2 papers with code
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers.
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies
As case studies, we consider how momentum can improve the architecture design for recurrent neural networks (RNNs), neural ordinary differential equations (ODEs), and transformers.
Heavy Ball Neural Ordinary Differential Equations
We propose heavy ball neural ordinary differential equations (HBNODEs), leveraging the continuous limit of the classical momentum accelerated gradient descent, to improve neural ODEs (NODEs) training and inference.
GRAND++: Graph Neural Diffusion with A Source Term
We propose GRAph Neural Diffusion with a source term (GRAND++) for graph deep learning with a limited number of labeled nodes, i. e., low-labeling rate.
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