Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...
Abstract: With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP ...
This is the Matlab Package for the Online Sparse Dictionary Learning (OSDL) algorithm, presented in: J. Sulam, B. Ophir, M. Zibulevsky and M. Elad, "Trainlets: Dictionary Learning in High Dimensions," ...
In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better ...
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...
Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and ...
The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an ...
DR-NMF is a recurrent neural network constructed from the unfolded iterations of the iterative soft-thresholding algorithm (ISTA) applied to sparse NMF inference. Sparse NMF inference is the task of ...
Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a ...
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