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Found 4 papers, 1 papers with code
Tree-based Ensemble Learning for Out-of-distribution Detection
In this paper, we propose TOOD detection, a simple yet effective tree-based out-of-distribution (TOOD) detection mechanism to determine if a set of unseen samples will have similar distribution as of the training samples.
Maximal Volume Matrix Cross Approximation for Image Compression and Least Squares Solution
Our main results consist of an improvement of a classic estimate for matrix cross approximation and a greedy approach for finding the maximal volume submatrices.
Semi-supervised Local Cluster Extraction by Compressive Sensing
Local clustering problem aims at extracting a small local structure inside a graph without the necessity of knowing the entire graph structure.
A Compressed Sensing Based Least Squares Approach to Semi-supervised Local Cluster Extraction
A least squares semi-supervised local clustering algorithm based on the idea of compressed sensing is proposed to extract clusters from a graph with known adjacency matrix.
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