Paper Citation Record · LEDGER

Adapting Auxiliary Losses Using Gradient Similarity

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1812.02224.

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

pith.paper-citation-record.v1
1812.02224 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:34:34.373838Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

95
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 76b2cee8-cc0e-463e-9b1f-ffa00e610efd · inbound

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss cites this paper.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Adapting Auxiliary Losses Using Gradient Similarity

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:45:58.545952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52d1e052-1648-407e-a701-f30c4224a965 · inbound

A Survey of State Representation Learning for Deep Reinforcement Learning cites this paper.

A Survey of State Representation Learning for Deep Reinforcement Learning Adapting Auxiliary Losses Using Gradient Similarity

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T23:34:34.373838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a38369b5-c204-4799-b2bc-343d11fd34a1 · inbound

Understanding Knowledge Transferability for Transfer Learning: A Survey cites this paper.

Understanding Knowledge Transferability for Transfer Learning: A Survey Adapting Auxiliary Losses Using Gradient Similarity

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:22:24.333492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f23cd41-20b3-48c6-9f52-3558f0d4e668 · inbound

Non-conflicting Energy Minimization in Reinforcement Learning based Robot Control cites this paper.

Non-conflicting Energy Minimization in Reinforcement Learning based Robot Control Adapting Auxiliary Losses Using Gradient Similarity

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T12:16:02.213551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58e705e8-8057-4bf4-9957-7c9dcc7d57a5 · inbound

Unifying Search and Recommendation in LLMs via Gradient Multi-Subspace Tuning cites this paper.

Unifying Search and Recommendation in LLMs via Gradient Multi-Subspace Tuning Adapting Auxiliary Losses Using Gradient Similarity

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T10:41:38.684880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79c6b7ad-f3a8-4f75-bb99-e8a529818bc7 · inbound

Lorentz Framework for Semantic Segmentation cites this paper.

Lorentz Framework for Semantic Segmentation Adapting Auxiliary Losses Using Gradient Similarity

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:11:53.188474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 70316813-0b1e-4947-ac4e-0fdb89d36d78 · inbound

When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining cites this paper.

When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining Adapting Auxiliary Losses Using Gradient Similarity

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:54.976625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a8bacf80-908c-47b5-a077-63ca47e32fff · inbound

Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations cites this paper.

Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations Adapting Auxiliary Losses Using Gradient Similarity

Reference 133

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:29:41.800871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f9a6ac86-6df1-4112-9bc4-6de424a17683 · inbound

Mitigating Gradient Pathology in PINNs through Aligned Constraint cites this paper.

Mitigating Gradient Pathology in PINNs through Aligned Constraint Adapting Auxiliary Losses Using Gradient Similarity

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:44:39.342787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56e6f109-a127-4da5-9ef3-235f6f85291b · inbound

T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation cites this paper.

T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation Adapting Auxiliary Losses Using Gradient Similarity

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.938395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation adced5e6-0222-47ea-89d0-6cd9c6ccc099 · inbound

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation cites this paper.

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation Adapting Auxiliary Losses Using Gradient Similarity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T02:22:20.429673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.