Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.17345.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:01:32.817933Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T00:37:54.229128Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 27519156-7707-401a-8bc5-4e4e5f7c6ba8 · inbound
Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions cites this paper.
Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:33:20.011219Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5e15d57-e9c6-4c24-8081-270d8c16190a · inbound
Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis cites this paper.
Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:01:32.817933Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ff6e792-6767-4f1c-ac44-f1287e388c88 · inbound
Generating Energy-Efficient Code via Large-Language Models -- Where are we now? cites this paper.
Generating Energy-Efficient Code via Large-Language Models -- Where are we now? Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:00:17.944814Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 67465110-5db6-403c-be65-3e2118e0141a · inbound
Generating Energy-Efficient Code via Large-Language Models -- Where are we now? cites this paper.
Generating Energy-Efficient Code via Large-Language Models -- Where are we now? Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:00:17.948477Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 144c0110-255f-4ab8-93a4-ca1c13a5b306 · inbound
ASSESSING THE STOCHASTIC PROPERTIES OF MODERN PSEUDO-RANDOM GENERATORS FOR PARALLEL COMPUTING cites this paper.
ASSESSING THE STOCHASTIC PROPERTIES OF MODERN PSEUDO-RANDOM GENERATORS FOR PARALLEL COMPUTING Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T00:37:54.232407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.