Paper Citation Record · LEDGER

SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1912.12355.

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

pith.paper-citation-record.v1
1912.12355 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:52:23.935904Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:50:11.092960Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 dfbcdf64-3df0-475e-81dc-6122796b4ab4 · inbound

Deep regularization networks for inverse problems with noisy operators cites this paper.

Deep regularization networks for inverse problems with noisy operators SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:23.935904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b2dbe90-6ca6-4e6b-a958-d44feaadafe2 · inbound

Auto-Adaptive PINNs with Applications to Phase Transitions cites this paper.

Auto-Adaptive PINNs with Applications to Phase Transitions SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:50:51.676399Z

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 c97bbab2-04c4-442a-a76e-c25131333ac0 · inbound

Physics-informed neural networks for form-finding of unilateral membrane structures cites this paper.

Physics-informed neural networks for form-finding of unilateral membrane structures SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:34:47.207748Z

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 cabf8c80-c2f9-4396-a103-e32bdfee8a21 · inbound

Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View cites this paper.

Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.698638Z

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 d1792d38-6ffb-42b1-a257-9108046d8a0f · inbound

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks cites this paper.

Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:46:26.650350Z

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 8757d27c-8e3d-4294-a396-56098310fd6d · inbound

Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion cites this paper.

Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:27:22.669031Z

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 615bc3b7-b565-4cf1-8781-1d9537098e94 · inbound

Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport cites this paper.

Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:49:19.483609Z

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 fffd1443-7ab2-4be1-97c7-e274c77ceaca · inbound

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery cites this paper.

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T08:19:44.099137Z

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 127b0583-ba42-4105-ba33-c3bcfaf77c2d · inbound

G-PINNs: Gaussian-based spatially weighted formulation for PINNs: 1D low-viscous Burgers cites this paper.

G-PINNs: Gaussian-based spatially weighted formulation for PINNs: 1D low-viscous Burgers SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:50:11.095098Z

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 b01fd583-df90-4add-9240-5e1b288b14c4 · inbound

Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows cites this paper.

Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T05:17:59.686836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.