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Paper Citation Record · LEDGER

Hypernetworks for Continual Semi-Supervised Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.01856.

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

pith.paper-citation-record.v1
2110.01856 v1

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 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:47:40.123803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T05:43:56.454243Z

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 20e69435-dec0-4dce-b7d5-e4ad23edfd25 · inbound

Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model cites this paper.

Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model Hypernetworks for Continual Semi-Supervised Learning

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:43:56.457049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-24T05:43:26.002963Z digest=sha256:52bf04ff0b4239f9f802d160307d0d8ca58060fce9d8e8f0bec3e49c5292c9ab

Observation 38341ccb-e3c6-4e31-a930-dc7e1f4c1d8b · inbound

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data cites this paper.

TabICL: A Tabular Foundation Model for In-Context Learning on Large Data Hypernetworks for Continual Semi-Supervised Learning

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-20T13:35:02.308250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-20T13:35:02.018244Z digest=sha256:6dd973ecd4e943bbf1a750445f7d19edff3e49d27cb3c4d5a1f87b2b97e7e3a4

Observation 8a1b8b38-d42c-4cab-8664-acb93f314db2 · inbound

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification cites this paper.

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification Hypernetworks for Continual Semi-Supervised Learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T20:47:40.123803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:47:40.123803Z digest=sha256:783ef5d19fe8c44f0a2b75f673d15535810489165766831e27086a0a690b3dec

Observation 05cc8c04-9bc8-4da8-9afa-9ed8c2cb18a5 · inbound

CLA: Latent Alignment for Online Continual Self-Supervised Learning cites this paper.

CLA: Latent Alignment for Online Continual Self-Supervised Learning Hypernetworks for Continual Semi-Supervised Learning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T17:37:52.759057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:37:52.759057Z digest=sha256:91fefb24cde5cddaa73362eeb17588efb0f0c5642e1c2bfa38f111d10eee2a43

Observation 7420387c-2348-4b55-98ed-6936a7e28b89 · inbound

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning cites this paper.

Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning Hypernetworks for Continual Semi-Supervised Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T23:28:11.871415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T23:28:11.871415Z digest=sha256:fc18a2e46f82762b7611c8fc590443211f68849fd66c78f67e1c088261bf9487