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Paper Citation Record · LEDGER

Deep Gaussian Mixture Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1711.06929.

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

pith.paper-citation-record.v1
1711.06929 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:28:33.237792Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 90826d28-401a-42e3-9c31-88e8c2daeb9f · inbound

SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval cites this paper.

SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval Deep Gaussian Mixture Models

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-11T11:28:33.237792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:28:33.237792Z digest=sha256:62a09a098967302468d4d69653c278680661b324113bad968dcd66a847be8395

Observation eb681c8a-73da-43c8-a12a-a46eb5c6e190 · inbound

Improving Environment Novelty Quantification for Effective Unsupervised Environment Design cites this paper.

Improving Environment Novelty Quantification for Effective Unsupervised Environment Design Deep Gaussian Mixture Models

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-08T18:17:14.775660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:17:14.775660Z digest=sha256:8199ae3391cc32e495a7dfc01d695f7d16dfd2720ad87ed57940fffb1b72e18d

Observation 50e5fbb8-4779-47bb-8726-04a4174bdd30 · inbound

IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections cites this paper.

IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections Deep Gaussian Mixture Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T05:03:00.011127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:03:00.011127Z digest=sha256:c5d2814ad91c12b7edf75ad587d080ff2ebe8c1c984ab6b707346933dce36291

Observation 642f808f-4ed5-4086-a28a-694c80d6d378 · inbound

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning cites this paper.

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning Deep Gaussian Mixture Models

Reference 148

Resolution
unresolved
no resolver link, observed 2026-08-05T22:03:09.876233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:03:09.876233Z digest=sha256:672c995d7678abb682e53f87a37592197e8e9ff8acda7acb2354ace7031cdfe2

Observation 83961629-be43-41ae-ae5c-5398f5dcc914 · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters Deep Gaussian Mixture Models

Reference 85

Resolution
verified exact
local_arxiv, observed 2026-06-28T07:41:44.813694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-28T07:41:38.548022Z digest=sha256:bc38a09e4be624ef2b6b3bd888be7441177125f0d5cef8e22b192b90412ef038

Observation 1d562b6f-640e-4c6e-9d94-5926e60e32ee · inbound

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters cites this paper.

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters Deep Gaussian Mixture Models

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-02T12:30:44.173564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:30:44.173564Z digest=sha256:be7534c28e5cdc58c80aa411b674337d907003f88b6d086f56a4ce5e081587e1