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Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits
In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model.
Supervised learning of sparse context reconstruction coefficients for data representation and classification
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.
Sparse Coding with Earth Mover's Distance for Multi-Instance Histogram Representation
It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error.
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