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

Population codes enable learning from few examples by shaping inductive bias

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks

Deep learning in optical metrology: a review

PDF) Adaptive Hashing With Sparse Matrix Factorization

Locality sensitive hashing with bit selection

Integrated Sensing and Communications With Reconfigurable Intelligent Surfaces

Group sparse based locality – sensitive dictionary learning for video semantic analysis

CausalBank

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

Performance of two complementary machine-learned potentials in modelling chemically complex systems

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

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

PDF) Graph PCA Hashing for Similarity Search (2017), Xiaofeng Zhu

PDF] Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines