Paper Citation Record · LEDGER

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings

As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.24281.

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

pith.paper-citation-record.v1
2505.24281 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:34:08.666053Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

12 of 12 outbound references displayed

verified exact0
verified fuzzy7
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab8810ec-ed57-4383-a9e8-f3285f344264 · outbound

This paper cites For example, Huang et al.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings For example, Huang et al

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:10.727026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31c30056-bc4c-442c-911d-892484453df0 · outbound

This paper cites For instance, Tang and Song (2016) proposed a regularized fusion method to identify and merge inter-task homogeneous parameter clusters in regression analysis.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings For instance, Tang and Song (2016) proposed a regularized fusion method to identify and merge inter-task homogeneous parameter clusters in regression analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:10.562013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31aa809b-72a8-491a-a050-7b9f215aeb39 · outbound

This paper cites an unresolved cited work.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:34:10.390139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9d1bf2e-35a3-492d-a7dc-5a85874f5c6d · outbound

This paper cites an unresolved cited work.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:34:10.212812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66c89bdc-e465-4290-a05d-249dd0e23070 · outbound

This paper cites an unresolved cited work.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:34:10.052898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c52c54db-76ed-497a-9fa3-486fa1897bf4 · outbound

This paper cites However, these methods can sometimes lead to unstable training due to the complexity of managing gradients 38 across diverse tasks (Wang and Tsvetkov, 2021).

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings However, these methods can sometimes lead to unstable training due to the complexity of managing gradients 38 across diverse tasks (Wang and Tsvetkov, 2021)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:09.920699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 732f03ac-57ac-4e84-ae56-09d5e01e1359 · outbound

This paper cites an unresolved cited work.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings Unresolved cited work

Reference 7

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:34:09.752956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95ea657b-2116-46b3-beea-1f90ab72a083 · outbound

This paper cites (2020); Watkins et al.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings (2020); Watkins et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:09.442519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a1899f4-1484-4ebc-b123-c628ca39544a · outbound

This paper cites In contrast, our bound only considers the complexity of A(S) and B 46 for n0 samples and C for N samples.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings In contrast, our bound only considers the complexity of A(S) and B 46 for n0 samples and C for N samples

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:09.300740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62f0eb79-e2b4-47df-928d-3551e6a1877b · outbound

This paper cites The fixed point r∗ 1 depends on the complexity of A(S) + B( bC), measured with respect to the sample size n0 of the new task along with other specific parameters.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings The fixed point r∗ 1 depends on the complexity of A(S) + B( bC), measured with respect to the sample size n0 of the new task along with other specific parameters

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:09.110695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d6be23f-22b7-408c-8dff-de10ba01f6ab · outbound

This paper cites sup βr∈B 1 nr nrX i=1 ιirC(X ri)βr # ≤max r∈[R] Bβ +B c max nr vuutEι.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings sup βr∈B 1 nr nrX i=1 ιirC(X ri)βr # ≤max r∈[R] Bβ +B c max nr vuutEι

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:34:08.891702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0efce28-5379-4e90-bb1e-b311d41fbb77 · outbound

This paper cites an unresolved cited work.

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings Unresolved cited work

Reference 2004

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:34:09.617416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.