SLCA incorporates a second layer of neurons which produce approxi- mately invariant responses to signal variations that are linear in their corresponding subspaces, such as phase shifts, resembling a hallmark characteristic of complex cells in V1.
Jun 18, 2020 · We introduce subspace locally competitive algorithms (SLCAs), a family of novel network architectures for modeling latent representations of ...
Abstract: We introduce subspace locally competitive algorithms (SLCAs), a family of novel network architectures for modeling latent representations of natural ...
Topics · Locally Competitive Algorithm · Subspace · Latent Representation · Phase Shifts · Simple-cell · Smallest Lowest Common Ancestor · Independent Subspace ...
In accordance with previous work, we believe these results to be general to networks with similar response surface curvature. However, more work must be ...
Journal Name: NICE '20: Proceedings of the Neuro-inspired Computational Elements Workshop ; Issue: 9 ; Page Range / eLocation ID: 1 to 8 ; Format(s):: Medium: X.
Jul 22, 2019 · The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding.
Missing: Subspace | Show results with:Subspace
Compared with other state-of-art methods, this algorithm provides improved segmentation, especially when the minimal subspaces are significantly intersected ...
This method strives to preserve the locally geometrical structure of the obtained subspace via neighborhood patches while projecting the nonnegative data points ...
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Jul 23, 2019 · The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding.
Missing: Subspace | Show results with:Subspace