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Found 3 papers, 1 papers with code
Generalizable autoregressive modeling of time series through functional narratives
In this work, we propose a novel objective for transformers that learn time series by re-interpreting them as temporal functions.
Struggle with Adversarial Defense? Try Diffusion
Unlike data-driven classifiers, TMDC, guided by Bayesian principles, utilizes the conditional likelihood from diffusion models to determine the class probabilities of input images, thereby insulating against the influences of data shift and the limitations of adversarial training.
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance.
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