Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1912.12355.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:33:07.574830Z
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
- 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 69c2ca4d-6844-43d7-8aa4-350da4271176 · inbound
Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations cites this paper.
Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:33:07.574830Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 230690de-7420-4335-88a1-7fffaa2c0f1e · inbound
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components cites this paper.
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:36:54.930872Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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.