Paper Citation Record · LEDGER

Mixed Precision DNNs: All you need is a good parametrization

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1905.11452.

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

pith.paper-citation-record.v1
1905.11452 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:25:30.152432Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:51:24.367342Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 187412a1-228c-488c-a793-53b23c93f8d3 · inbound

Towards Accurate and Efficient Sub-8-Bit Integer Training cites this paper.

Towards Accurate and Efficient Sub-8-Bit Integer Training Mixed Precision DNNs: All you need is a good parametrization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T19:13:10.513888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59935be3-58d0-4937-a913-35dbd41dccee · inbound

A 1Mb mixed-precision quantized encoder for image classification and patch-based compression cites this paper.

A 1Mb mixed-precision quantized encoder for image classification and patch-based compression Mixed Precision DNNs: All you need is a good parametrization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:23:49.838976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad7c615-c3c3-463e-bcc5-0877934bf831 · inbound

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning cites this paper.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Mixed Precision DNNs: All you need is a good parametrization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.152432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9856914-0fc4-41e5-9e00-f83f46834d5d · inbound

Harnessing Input-Adaptive Inference for Efficient VLN cites this paper.

Harnessing Input-Adaptive Inference for Efficient VLN Mixed Precision DNNs: All you need is a good parametrization

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T21:16:26.312095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9270761-28f7-4899-8882-b49d59a80082 · inbound

CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs cites this paper.

CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs Mixed Precision DNNs: All you need is a good parametrization

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:51:24.369595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3817aef2-eb59-4834-bc59-b033c4cbb895 · inbound

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning cites this paper.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Mixed Precision DNNs: All you need is a good parametrization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.853311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edb3abc0-cfcd-4a32-84af-41b9b5e7bfe1 · inbound

SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features cites this paper.

SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features Mixed Precision DNNs: All you need is a good parametrization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:03.952100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.