Pith. sign in

REVIEW 1 cited by

Log-time and Log-space Extreme Classification

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1611.01964 v1 pith:YZZWGP5C submitted 2016-11-07 cs.LG

classification cs.LG
keywords ltlsclassificationinferencemulticlassmultilabelpredictionproblemsalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We present LTLS, a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. We train LTLS with stochastic gradient descent on a number of multiclass and multilabel datasets and show that despite its small memory footprint it is often competitive with existing approaches.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

    cs.IR 2026-07 conditional novelty 6.0 of 10

    OneShot trains hierarchical item codebooks jointly with the ranking loss, enabling nonlinear neural scoring in billion-scale retrieval and reporting +20% recall, 10x fewer dense-ranked items, and live Instagram gains.

Pith tools