atlas

Research map / Learning theory and algorithms / Sample complexity

Practical Data-Dependent Metric Compression with Provable Guarantees

NeurIPS 2017 · Learning theory and algorithms › Sample complexity · approach: Statistical-query lower bounds · 2 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use 16 blocks instead of 1 (ablation)QuadSketch · SIFTbits per coordinate: 5.17 -> 2.795Helped
Vary the number of blocks (ablation)QuadSketch · SIFTaccuracy: 0.719; 0.717; 0.722; 0.712; 0.712; 0.712; 0.713; 0.72No clear effect
Increase the number of blocks (ablation)QuadSketch · SIFTbits per coordinate: 5.17; 4.523; 4.02; 3.272; 2.795; 3.474; 4.032; 4.079Mixed
Increase the pruning parameter beyond 6 (ablation)QuadSketch · SIFTsketch sizeNo clear effect
Use QuadSketch rather than Product Quantizationmetric compression on SIFT and MNIST · SIFT and MNISTaccuracyMixed
Use QuadSketch rather than Product Quantizationmetric compression · NYC TaxiaccuracyHelped
Use QuadSketch rather than Product Quantizationmetric compression · DiagonalaccuracyHelped
Use QuadSketch or Product Quantization rather than uniform scalar quantizationmetric compression · SIFT, MNIST, NYC Taxi, and DiagonalaccuracyHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy