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Found 3 papers, 1 papers with code
On Tail Decay Rate Estimation of Loss Function Distributions
The study of loss function distributions is critical to characterize a model's behaviour on a given machine learning problem.
Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows
Furthermore, we derive the Jacobian determinant of the general augmented form by generalizing the chain rule in the continuous sense into the Cable Rule, which expresses the forward sensitivity of ODEs with respect to their initial conditions.
Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching
In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potentially impede modeling flexibility and capacity.
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