Paper Citation Record · LEDGER

You Only Train Once: Differentiable Subset Selection for Omics Data

As of 15 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2512.17678.

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

pith.paper-citation-record.v1
2512.17678 v2

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T15:13:58.965767Z

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

9 of 9 outbound references displayed

verified exact0
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ded4178-56d1-446d-861d-bd8d6dbce015 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

You Only Train Once: Differentiable Subset Selection for Omics Data Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:57.987208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92ee533d-f2c3-4345-b9e7-1d8616b47610 · outbound

This paper cites N-act: An interpretable deep learning model for automatic cell type and salient gene identification.

You Only Train Once: Differentiable Subset Selection for Omics Data N-act: An interpretable deep learning model for automatic cell type and salient gene identification

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.544321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdd69254-dcdd-450a-9176-8cd38ffdb192 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

You Only Train Once: Differentiable Subset Selection for Omics Data The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.739843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a9a3311-c302-4102-bf25-7f31f9f55733 · outbound

This paper cites Open Access uptake by universities worldwide.

You Only Train Once: Differentiable Subset Selection for Omics Data Open Access uptake by universities worldwide

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.436455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6110c0ae-360e-4878-9825-61375328caf6 · outbound

This paper cites Model-Agnostic Interpretability of Machine Learning.

You Only Train Once: Differentiable Subset Selection for Omics Data Model-Agnostic Interpretability of Machine Learning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.869972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69b11fd8-f599-4df4-a853-fe8b8d2dbaf0 · outbound

This paper cites Comprehensive integration of single-cell data.cell, 177(7):1888–1902,.

You Only Train Once: Differentiable Subset Selection for Omics Data Comprehensive integration of single-cell data.cell, 177(7):1888–1902,

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.965767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf84d2b3-c562-48b2-a699-a2ebdf39218f · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

You Only Train Once: Differentiable Subset Selection for Omics Data Categorical Reparameterization with Gumbel-Softmax

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.636952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f14c0f4-315f-4a4c-9172-d4bde4b173c5 · outbound

This paper cites Predictive and robust gene selection for spatial transcriptomics.Nature Communications, 14(1):2091,.

You Only Train Once: Differentiable Subset Selection for Omics Data Predictive and robust gene selection for spatial transcriptomics.Nature Communications, 14(1):2091,

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.189837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50532dee-b711-478f-b771-603b8e72f191 · outbound

This paper cites an unresolved cited work.

You Only Train Once: Differentiable Subset Selection for Omics Data Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T15:13:58.318438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.