Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.19211.
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, observed 2026-08-12T17:55:20.852458Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T23:46:09.743081Z
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 9e4d4d91-40b4-43c5-8bbc-3a7656204d77 · inbound
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification cites this paper.
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:53:45.893821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f16b0962-5c0e-48d4-bae2-16fa77fe3e4b · inbound
A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation cites this paper.
A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:55:20.852458Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9bb47e28-f1d5-4dd9-a322-c1aacb4fbed3 · inbound
Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction cites this paper.
Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:55:17.996178Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 68569e2f-da55-4781-9719-0fa386f6e81e · inbound
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches cites this paper.
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T13:01:12.871314Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 342d2b29-6344-4ff1-a341-dc6784cf2aaa · inbound
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction cites this paper.
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T23:46:09.750780Z
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.