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Casting graph isomorphism as a point set registration problem using a simplex embedding and sampling
Graph isomorphism is an important problem as its worst-case time complexity is not yet fully understood.
On the Preservation of Spatio-temporal Information in Machine Learning Applications
In conventional machine learning applications, each data attribute is assumed to be orthogonal to others.
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification
Furthermore, an evolutionary approach can be chosen to determine the number and the dimensionality of simplices composing the simplicial, in which most generative and compact simplicials are favored.
Self-recognition in conversational agents
Sustaining the idea of self within the Turing test is still possible if the judge decides to act as a textual mirror.
Convolutional Neural Networks: A Binocular Vision Perspective
It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain.
Boosting Dictionary Learning with Error Codes
In conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand.
Depth Estimation from Single Image using Sparse Representations
Monocular depth estimation is an interesting and challenging problem as there is no analytic mapping known between an intensity image and its depth map.
