Paper Citation Record · LEDGER

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs

As of 23 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2505.13060.

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

pith.paper-citation-record.v1
2505.13060 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:26:47.130594Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

11 of 11 outbound references displayed

verified exact2
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21ca0e69-8f47-4f31-a077-e0340acc2ec8 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.105679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ecb2331-7dbe-431d-94f3-65a26a02c762 · outbound

This paper cites Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.113077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2740a60-beb2-4c97-93ac-0660e6b21652 · outbound

This paper cites A White Paper on Neural Network Quantization.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs A White Paper on Neural Network Quantization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.116858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79898396-d056-40bc-be92-2f22609b7df5 · outbound

This paper cites A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:26:47.193639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2cc9a2f0-aecf-4ec5-8ee9-699ada4c367d · outbound

This paper cites doi: 10.1145/3474381.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs doi: 10.1145/3474381

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.123758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21e09a4c-352b-430e-8a12-4d6480de9e8a · outbound

This paper cites Neural Network Quantization for Efficient Inference: A Survey.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs Neural Network Quantization for Efficient Inference: A Survey

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.127024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daba71b3-0f6f-4866-9fa0-1811ceefa1ac · outbound

This paper cites A Survey on Methods and Theories of Quantized Neural Networks.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs A Survey on Methods and Theories of Quantized Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.109355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d76ebd66-c7b5-4f40-8070-abc555ac5837 · outbound

This paper cites Post-training 4-bit quantization of convolution networks for rapid-deployment.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs Post-training 4-bit quantization of convolution networks for rapid-deployment

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.098336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ace20f62-7279-4fab-8468-8160cd167bce · outbound

This paper cites Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs Towards Mixed-Precision Quantization of Neural Networks via Constrained Optimization

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:26:47.254003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9ccf9268-b262-4640-92ad-41ce913d4410 · outbound

This paper cites EfQAT: An Efficient Framework for Quantization-Aware Training.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs EfQAT: An Efficient Framework for Quantization-Aware Training

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T20:26:47.095465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3a63e34-f40d-4ec6-a3b0-cdf127599aa8 · outbound

This paper cites StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign.

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T20:26:47.170372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.