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Found 6 papers, 0 papers with code
Observation-specific explanations through scattered data approximation
This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction process.
Fast Empirical Scenarios
We seek to extract a small number of representative scenarios from large and high-dimensional panel data that are consistent with sample moments.
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
Leveraging on the sparse representation of kernel matrices in samplet coordinates, this approach converges faster than the fast iterative shrinkage thresholding algorithm and is feasible for large-scale data.
Anisotropic multiresolution analyses for deepfake detection
We employ the fully separable wavelet transform and multiwavelets to obtain the anisotropic features to feed to standard CNN classifiers.
Adaptive joint distribution learning
We develop a new framework for embedding joint probability distributions in tensor product reproducing kernel Hilbert spaces (RKHS).
Samplets: A new paradigm for data compression
In this article, we introduce the concept of samplets by transferring the construction of Tausch-White wavelets to the realm of data.
