Paper Citation Record · LEDGER

Zero-bias autoencoders and the benefits of co-adapting features

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1402.3337.

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

pith.paper-citation-record.v1
1402.3337 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:36:43.236490Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-25T05:36:40.370472Z

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 14bb9ff2-7e72-4c09-816a-86bce35340ee · inbound

Scaling and evaluating sparse autoencoders cites this paper.

Scaling and evaluating sparse autoencoders Zero-bias autoencoders and the benefits of co-adapting features

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T17:47:23.150641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 913927dc-5e32-4662-9b0c-5412f9705a85 · inbound

UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs cites this paper.

UnIT: Scalable Unstructured Inference-Time Pruning for MAC-efficient Neural Inference on MCUs Zero-bias autoencoders and the benefits of co-adapting features

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:36:43.236490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df165ed9-7b3b-4305-a405-df0a83379816 · inbound

PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding cites this paper.

PolySAE: Modeling Feature Interactions in Sparse Autoencoders via Polynomial Decoding Zero-bias autoencoders and the benefits of co-adapting features

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:48:08.428667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fce57a0-da47-4b76-90ab-55f517ae3244 · inbound

Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery cites this paper.

Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery Zero-bias autoencoders and the benefits of co-adapting features

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:21:24.903043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ab5d10eb-bdf3-4071-ac0c-1996da9052af · inbound

Steered Generation via Gradient-Based Optimization on Sparse Query Features cites this paper.

Steered Generation via Gradient-Based Optimization on Sparse Query Features Zero-bias autoencoders and the benefits of co-adapting features

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-25T05:36:40.372461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.