Integrity report for AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.
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0Advisory
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—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/GVXRW2T6U6QEP4N7SWIZH2QNSH/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.