Paper Citation Record · LEDGER

Divide and not forget: Ensemble of selectively trained experts in Continual Learning

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.10191.

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

pith.paper-citation-record.v1
2401.10191 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:57:31.249346Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:35:42.200703Z

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 62c38501-8b31-4da3-b3b9-58bcf45bb3a9 · inbound

Expert Routing with Synthetic Data for Continual Learning cites this paper.

Expert Routing with Synthetic Data for Continual Learning Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T05:57:31.249346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d245cc-f501-41fb-9b2d-98743d24fe9e · inbound

Mixture of Experts (MoE): A Big Data Perspective cites this paper.

Mixture of Experts (MoE): A Big Data Perspective Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 140

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.721095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2560474-ff1d-4539-b0ed-0f5dd7076423 · inbound

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective cites this paper.

Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T18:23:21.891173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2dc3cdf-fb1e-487e-b32a-b8c8f1c56a20 · inbound

Continual Learning via Ensemble-Based Depth-Wise Masked Autoencoders for Data Quality Monitoring in High-Energy Physics cites this paper.

Continual Learning via Ensemble-Based Depth-Wise Masked Autoencoders for Data Quality Monitoring in High-Energy Physics Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T19:26:50.529910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20094d6d-53fd-431c-8cc7-297bec8bc5b5 · inbound

FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning cites this paper.

FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:31:25.610086Z

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.

Observation 8796bf65-d6c7-4cd6-bfa5-d0b097260ab2 · inbound

FaceMoE: Mixture of Experts for Low-Resolution Face Recognition cites this paper.

FaceMoE: Mixture of Experts for Low-Resolution Face Recognition Divide and not forget: Ensemble of selectively trained experts in Continual Learning

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:35:42.202191Z

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.