Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
End-to-end learning for music audio tagging at scale
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1711.02520.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T11:32:35.784317Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T01:05:36.526806Z
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 d30f4abe-f4b4-4ad4-aa0d-87d1c40c333d · inbound
Lukthung Classification Using Neural Networks on Lyrics and Audios cites this paper.
Lukthung Classification Using Neural Networks on Lyrics and Audios End-to-end learning for music audio tagging at scale
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T11:32:35.784317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f42f745-4e9a-400f-961e-9cf3f1de5ddc · inbound
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases cites this paper.
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases End-to-end learning for music audio tagging at scale
Reference 294
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T20:23:11.334625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80d5344d-843b-4a97-938b-4092e8bf42ed · inbound
Vision Language Models Are Few-Shot Audio Spectrogram Classifiers cites this paper.
Vision Language Models Are Few-Shot Audio Spectrogram Classifiers End-to-end learning for music audio tagging at scale
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T18:02:17.876163Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 172dd704-c55e-4cd5-a844-f4416438b873 · inbound
Leave-One-EquiVariant: Alleviating invariance-related information loss in contrastive music representations cites this paper.
Leave-One-EquiVariant: Alleviating invariance-related information loss in contrastive music representations End-to-end learning for music audio tagging at scale
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T01:05:36.537014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.