Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:06:21.678575Z
Paper Citation Record · LEDGER
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy
As of 16 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:1908.05874.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
2 of 2 outbound references displayed
- verified exact0
- verified fuzzy1
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d4e3afce-2891-4395-8798-34062267cb1b · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:06:21.678575Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c43106dd-c6b9-421c-ae20-372385d1df5d · outbound
This paper cites MSE loss is a generalized, domain-agnostic loss function that can be applied to many problems in many domains.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy MSE loss is a generalized, domain-agnostic loss function that can be applied to many problems in many domains
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T13:06:21.727966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.