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Found 6 papers, 2 papers with code
Eliminating Lipschitz Singularities in Diffusion Models
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models.
DAFD: Domain Adaptation via Feature Disentanglement for Image Classification
This so-called domain shift leads to a significant performance drop in image classification.
Dimensionality-Varying Diffusion Process
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension.
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining
In this paper, we find that the spreadsheet formula, which performs calculations on numerical values in tables, is naturally a strong supervision of numerical reasoning.
Computationally Efficient Learning of Statistical Manifolds
By exploiting the connection between Hellinger/total variation distance for discrete distributions and the L2/L1 norm, we demonstrate that the proposed distance estimators, combined with approximate nearest neighbor searching, could largely improve the computational efficiency with little to no loss in the accuracy of manifold embedding.
Few Shot Learning with Simplex
During testing, combined with the test sample and the points in the class, a new simplex is formed.
