Search Results for author:
Found 14 papers, 6 papers with code
Locally-Adaptive Quantization for Streaming Vector Search
In this work, we study LVQ in streaming similarity search.
LeanVec: Searching vectors faster by making them fit
In this work, we present LeanVec, a framework that combines linear dimensionality reduction with vector quantization to accelerate similarity search on high-dimensional vectors while maintaining accuracy.
CoLiDE: Concomitant Linear DAG Estimation
We deal with the combinatorial problem of learning directed acyclic graph (DAG) structure from observational data adhering to a linear structural equation model (SEM).
Similarity search in the blink of an eye with compressed indices
In this work, we present new techniques and systems for creating faster and smaller graph-based indices.
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.
Procrustean Orthogonal Sparse Hashing
We compare POSH, Binary OSL, and SphericalHash to several state-of-the-art hashing methods and provide empirical results for the superiority of the proposed methods across a wide range of standard benchmarks and parameter settings.
Do place cells dream of conditional probabilities? Learning Neural Nyström representations
We posit that hippocampal place cells encode information about future locations under a transition distribution observed as an agent explores a given (physical or conceptual) space.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i. e., they respond to a small neighborhood of stimulus space.
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling
In solving hard computational problems, semidefinite program (SDP) relaxations often play an important role because they come with a guarantee of optimality.
Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i. e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint.
Fast L1-NMF for Multiple Parametric Model Estimation
In this work we introduce a comprehensive algorithmic pipeline for multiple parametric model estimation.
Compressed Nonnegative Matrix Factorization is Fast and Accurate
To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable.
A Bi-clustering Framework for Consensus Problems
We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation.
Video Human Segmentation using Fuzzy Object Models and its Application to Body Pose Estimation of Toddlers for Behavior Studies
Video object segmentation is a challenging problem due to the presence of deformable, connected, and articulated objects, intra- and inter-object occlusions, object motion, and poor lighting.
