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Paper Citation Record · LEDGER

Understanding Trainable Sparse Coding via Matrix Factorization

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1609.00285.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1609.00285 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:15.552169Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T04:09:14.153415Z

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 2b107006-65a3-4952-94c2-d9a3544a5ae4 · inbound

Data-driven approaches to inverse problems cites this paper.

Data-driven approaches to inverse problems Understanding Trainable Sparse Coding via Matrix Factorization

Reference 1965

Resolution
verified exact
local_arxiv, observed 2026-08-07T04:09:14.242157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T04:09:10.103442Z digest=sha256:e0e3b0dd89a3f804a87c397f62ea9d40712264ce078872b6c9ca6b688344ed45

Observation caac32fe-da59-4a36-a272-c0efd64b4a3c · inbound

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions cites this paper.

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions Understanding Trainable Sparse Coding via Matrix Factorization

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-15T14:39:15.552169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:39:15.552169Z digest=sha256:d8cb92b33dc26f0879001f9cd2a63ce503d0a4d9bf0d1da90e17fc45e6749139