Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
$\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.01078.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:58:01.152395Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T13:28:19.627579Z
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 d603d38b-e9fa-442d-82b5-5ca45f391262 · inbound
Variational Encoder-Decoders for Learning Latent Representations of Physical Systems cites this paper.
Variational Encoder-Decoders for Learning Latent Representations of Physical Systems $\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:58:01.152395Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4cf500ef-4399-4da5-a44b-36aa503434d5 · inbound
Competing nonlinearities, criticality, and order-to-chaos transition in deep networks cites this paper.
Competing nonlinearities, criticality, and order-to-chaos transition in deep networks $\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:26:12.322198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a2858735-bb77-417f-9b52-f7c375f0c790 · inbound
Charting the emergent low-dimensional manifold of quantum materials cites this paper.
Charting the emergent low-dimensional manifold of quantum materials $\Gamma$-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T13:28:19.628995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.