Paper Citation Record · LEDGER

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data

As of 13 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2608.05930.

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

pith.paper-citation-record.v1
2608.05930 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:29:49.600746Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ee75edf-d966-414e-a5e9-2905881edad4 · outbound

This paper cites The Grow It! app— longitudinal changes in adolescent well-being during the COVID-19 pandemic: a proof-of-concept study.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data The Grow It! app— longitudinal changes in adolescent well-being during the COVID-19 pandemic: a proof-of-concept study

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.520371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 301d706c-e259-499b-8ff2-9e8d1f08f967 · outbound

This paper cites New developments in experience sampling methodology.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data New developments in experience sampling methodology

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.536450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4268264b-a8f9-41c8-b678-c074ff6907ca · outbound

This paper cites A solution for missing data in recurrent neural networks with an application to blood glucose prediction.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data A solution for missing data in recurrent neural networks with an application to blood glucose prediction

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.552185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fb40e2f-94ab-42b1-8ac3-89a55cb841e0 · outbound

This paper cites Speech recognition with missing data using recurrent neural nets.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Speech recognition with missing data using recurrent neural nets

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.556758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a29e97a3-46e6-498f-93c0-e553daf14c03 · outbound

This paper cites C.; Kale, D.; Wetzel, R.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data C.; Kale, D.; Wetzel, R

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.561525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 650987a4-083a-4ea6-838c-ae9321314d50 · outbound

This paper cites Recurrent neural networks for missing or asynchronous data.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Recurrent neural networks for missing or asynchronous data

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.546569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8263544d-6309-4cd2-add0-6c79c3cb4dc4 · outbound

This paper cites Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing

Reference 1412

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.590418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b399c3d-151c-4017-8e83-f44097ee03c4 · outbound

This paper cites A new class of stochastic EM algorithms.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data A new class of stochastic EM algorithms

Reference 1979

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.581161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82211fd4-93ea-415a-947b-7bf6d419e219 · outbound

This paper cites M.; Slasor, P.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data M.; Slasor, P

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.525749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eb5ed23-8047-4094-925d-a47ff3492b63 · outbound

This paper cites an unresolved cited work.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Unresolved cited work

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.576405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4edfbf2-ff55-4567-959f-5dd0dbbb35f3 · outbound

This paper cites Auto-Encoding Variational Bayes.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Auto-Encoding Variational Bayes

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.571367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a78da1-ccc6-459c-82fd-82c426f5d92a · outbound

This paper cites Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.600746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 823c29ad-4fa2-47ac-a6e1-cb0d835da252 · outbound

This paper cites Understanding disentangling in $\beta$-VAE.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Understanding disentangling in $\beta$-VAE

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.595554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a84e217-bc10-4953-9d58-6cc61e74c2f8 · outbound

This paper cites Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.541653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6868fa79-0092-42bf-b3df-cd1830d7fef8 · outbound

This paper cites Latent mixed-effect models for high-dimensional longitudinal data.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Latent mixed-effect models for high-dimensional longitudinal data

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.530719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cd1cf5a-7780-4151-af3c-f5acbfbb3d00 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data Adam: A Method for Stochastic Optimization

Reference 5111

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.585594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44c08266-54a3-4bc4-80f1-b233777cbf82 · outbound

This paper cites P.; Barnett, I.

Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data P.; Barnett, I

Reference 6085

Resolution: unresolved; no resolver link, observed 2026-08-10T04:29:49.566420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.