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Paper Citation Record · LEDGER

Universally Slimmable Networks and Improved Training Techniques

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1903.05134.

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

pith.paper-citation-record.v1
1903.05134 v2

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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:02:18.164755Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T03:10:53.338302Z

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 64eb06ae-2ccf-4a21-96a1-bba91e3d3a91 · inbound

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures cites this paper.

Towards Adaptive Deep Learning: Model Elasticity via Prune-and-Grow CNN Architectures Universally Slimmable Networks and Improved Training Techniques

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-15T21:02:18.164755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:02:18.164755Z digest=sha256:72a6dc76d137bfe747e20be899fa31d9956e6955c7e0225fe4ce8e9737e18322

Observation 58ff14a2-d840-482e-9860-fcd2a9071ca1 · inbound

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning cites this paper.

MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning Universally Slimmable Networks and Improved Training Techniques

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:10:53.339622Z

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.

source=pdf_text observed=2026-05-11T02:38:14.029660Z digest=sha256:67c9dd591a01559a5128cf323a9604accb364b6c4e99d14450ff238bddf4e90f