Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T05:01:06.769364Z
Paper Citation Record · LEDGER
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:1909.02210.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T00:05:14.914080Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T06:26:23.318288Z
Reference resolution
12 of 12 outbound references displayed
- verified exact1
- verified fuzzy1
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation bba7f0f2-607a-48c5-a5ee-9f80ea8edc9a · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Adam: A Method for Stochastic Optimization
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.749969Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40b05757-43a4-4649-a738-d5c520f0d5a9 · outbound
This paper cites How Well Generative Adversarial Networks Learn Distributions.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations How Well Generative Adversarial Networks Learn Distributions
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.757982Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea09aa00-0d92-4f40-8552-183b68f22f10 · outbound
This paper cites An empirical analysis of dropout in piecewise linear networks.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations An empirical analysis of dropout in piecewise linear networks
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.769364Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7e1c98a-c084-4cc7-99fc-35d0a778e92c · outbound
This paper cites Deep Neural Networks for Estimation and Inference.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Deep Neural Networks for Estimation and Inference
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T05:01:06.896013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d3d5b0bf-813c-4664-b89b-715e753fadfa · outbound
This paper cites CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.753721Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5c160767-deae-4c49-b104-1ce675330be6 · outbound
This paper cites Large-scale machine learning with stochastic gradient descent.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Large-scale machine learning with stochastic gradient descent
Reference 1974
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:01:06.934123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9313925f-a46d-4b6f-a0b8-f7e61f63cb82 · outbound
This paper cites Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Reference 1999
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.765516Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 94ff0a1b-37af-465c-a9f9-b1dc7be69c8f · outbound
This paper cites Conditional Generative Adversarial Nets.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Conditional Generative Adversarial Nets
Reference 2010
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.761818Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0153b66a-dd8b-41cd-9f92-df219e4bce43 · outbound
This paper cites How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
Reference 2013
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.745896Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 34269643-8b93-44d8-8afc-b52a9dcb6088 · outbound
This paper cites A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.741392Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95df93e3-16cf-4a0c-a3e0-4ad53b1843aa · outbound
This paper cites Wasserstein GAN.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Wasserstein GAN
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.729063Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 118fe51b-8a92-4136-8669-ba0b94c01cd6 · outbound
This paper cites Towards Principled Methods for Training Generative Adversarial Networks.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations Towards Principled Methods for Training Generative Adversarial Networks
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.724909Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 6a34e34f-b3cf-4d31-b6f6-405f738a7c99 · inbound
The Statistical Cost of Adaptation in Multi-Source Transfer Learning cites this paper.
The Statistical Cost of Adaptation in Multi-Source Transfer Learning Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Reference 226
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:26:23.320929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d00c5fec-4508-4a15-b877-78a5faa5e16b · inbound
Towards Optimal Estimators for Randomized Control Trials cites this paper.
Towards Optimal Estimators for Randomized Control Trials Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T00:05:14.914080Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.