Paper Citation Record · LEDGER

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks

As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.15753.

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

pith.paper-citation-record.v1
2607.15753 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:30:26.403560Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

38 of 38 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4d3da05c-e176-450b-8cf8-9cb5097dbfca · outbound

This paper cites Deep learning for computer vision: A brief review,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Deep learning for computer vision: A brief review,

Reference 1

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Observation 80657536-d947-42b8-8bd7-e10e4217119d · outbound

This paper cites MCUNet: Tiny Deep Learning on IoT Devices.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks MCUNet: Tiny Deep Learning on IoT Devices

Reference 2

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Observation 8407079a-fbb8-4d46-8dfc-943cb27897b5 · outbound

This paper cites Tas: ternarized neural architecture search for resource-constrained 9 edge devices,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Tas: ternarized neural architecture search for resource-constrained 9 edge devices,

Reference 3

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Observation a552f209-20f5-4417-b1d1-fac7e0b3840a · outbound

This paper cites Testability and depend- ability of ai hardware: Survey, trends, challenges, and perspectives,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Testability and depend- ability of ai hardware: Survey, trends, challenges, and perspectives,

Reference 4

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Observation 9e6da432-2ee0-4512-8f4e-fc4ff34e522a · outbound

This paper cites A systematic literature review on hardware reliability assessment methods for deep neural networks,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks A systematic literature review on hardware reliability assessment methods for deep neural networks,

Reference 5

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Observation 0538aff4-034e-46a3-9d47-9a021c3d4abd · outbound

This paper cites Resilience of deep learning applications: A systematic literature review of analysis and hard- ening techniques,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Resilience of deep learning applications: A systematic literature review of analysis and hard- ening techniques,

Reference 6

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Observation 6222cf9b-b585-49dc-8044-20a5c948a145 · outbound

This paper cites Soft errors in dnn accelerators: A comprehensive review,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Soft errors in dnn accelerators: A comprehensive review,

Reference 7

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Observation 0abdff69-3575-4569-afeb-2dce54c03c9b · outbound

This paper cites Fast and accurate error simulation for cnns against soft errors,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Fast and accurate error simulation for cnns against soft errors,

Reference 8

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This paper cites Impact of scaling on neutron-induced soft error in srams from a 250 nm to a 22 nm design rule,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Impact of scaling on neutron-induced soft error in srams from a 250 nm to a 22 nm design rule,

Reference 9

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This paper cites The impact of faults on dnns: A case study,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks The impact of faults on dnns: A case study,

Reference 10

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Observation 99a7070d-ea7c-46ab-9125-a5701f913175 · outbound

This paper cites Are cnns reliable enough for critical applications? an exploratory study,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Are cnns reliable enough for critical applications? an exploratory study,

Reference 11

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Observation aae91a77-ba0d-4939-81e9-6693ce8227c9 · outbound

This paper cites Application of artificial intelligence in healthcare: chances and challenges,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Application of artificial intelligence in healthcare: chances and challenges,

Reference 12

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Observation 5ed4b873-7189-4712-897c-b59c0a6a2204 · outbound

This paper cites A survey of human gait-based artificial intelli- gence applications,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks A survey of human gait-based artificial intelli- gence applications,

Reference 13

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This paper cites Time-series forecasting with deep learning: a survey,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Time-series forecasting with deep learning: a survey,

Reference 14

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Observation fbd34619-b8d1-489d-9dce-731134e6ef9c · outbound

This paper cites Analysis and improvement of resilience for long short-term mem- ory neural networks,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Analysis and improvement of resilience for long short-term mem- ory neural networks,

Reference 15

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Observation 11e9b6dc-52f6-4623-94dc-6e5cc4bf5ff4 · outbound

This paper cites Analysis and enhancement of resilience for lstm accelerators using residue-based ceds,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Analysis and enhancement of resilience for lstm accelerators using residue-based ceds,

Reference 16

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Observation c16cd334-f721-46f5-9841-37759f8d48d4 · outbound

This paper cites Zero-memory- overhead clipping-based fault tolerance for lstm deep neural net- works,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Zero-memory- overhead clipping-based fault tolerance for lstm deep neural net- works,

Reference 17

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Observation c36f7731-2747-4896-97e0-2aeaf5968ffd · outbound

This paper cites Soft error resilience analysis of lstm networks,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Soft error resilience analysis of lstm networks,

Reference 18

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Observation f96223d8-782b-464b-a27f-563a32a5f8ba · outbound

This paper cites Learning both weights and connections for efficient neural network,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Learning both weights and connections for efficient neural network,

Reference 19

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Observation 3a6eb798-9b0b-495a-ad4c-60b5f4936c0b · outbound

This paper cites Understanding deep learning requires rethinking generalization.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Understanding deep learning requires rethinking generalization

Reference 20

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Observation 86b4608f-969d-4927-a43d-7aff674429dc · outbound

This paper cites Zhang, Z.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Zhang, Z

Reference 21

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This paper cites AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks

Reference 22

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Understanding the difficulty of training deep feedforward neural networks,

Reference 23

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Goodfellow, Y

Reference 24

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Inception- v4, inception-resnet and the impact of residual connections on learning,

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 26

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Understanding Neural Networks Through Deep Visualization

Reference 27

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 28

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Deep learning,

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Reliability Improvement of Circular k-out-of-n: G Balanced Systems through Center of Gravity

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Unresolved cited work

Reference 31

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Observation b01b4fa6-8f75-46a7-aac5-efa19532bbf8 · outbound

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Under- standing error propagation in deep learning neural network (dnn) accelerators and applications,

Reference 32

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Observation 779532f1-8f99-4b2b-8d8f-6cb655e3d540 · outbound

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Minerva: Enabling low-power, highly-accurate deep neural network accelerators,

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Improving the fault tolerance of neural networks through weight clipping,

Reference 34

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks Mimic-iii, a freely accessible critical care database,

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CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks A data-driven framework for evaluating the robustness of ecg diagnosis algorithms,

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This paper cites A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 pa- tients,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 pa- tients,

Reference 37

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This paper cites A low-cost fault corrector for deep neural networks through range restriction,.

CoG-Guided Weight Correction for Fault-Tolerant Deep Neural Networks A low-cost fault corrector for deep neural networks through range restriction,

Reference 38

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Pith citing papers

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