pith. sign in

Integrity report for E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:1804.03836 · pith:2018:ASMTZQK5D2YRGEY6GPWZUSDMVO

0Critical
0Advisory
0Detectors run
Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/ASMTZQK5D2YRGEY6GPWZUSDMVO/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.