PDF] Locality-sensitive binary codes from shift-invariant kernels

This paper introduces a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel between the vectors. This paper addresses the problem of designing binary codes for high-dimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distribution-free encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shift-invariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent state-of-the-art method, spectral hashing.

PDF) A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

Locality-sensitive binary codes from shift-invariant kernels

Current progress and open challenges for applying deep learning across the biosciences

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Locality sensitive hashing with bit selection

Frontiers Quantitative approaches for decoding the specificity of the human T cell repertoire

A Complete List of All Adversarial Example Papers

Sparsity-promoting algorithms for the discovery of informative Koopman- invariant subspaces, Journal of Fluid Mechanics

Deep learning in optical metrology: a review

AI, Free Full-Text