Paper Citation Record · LEDGER

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations

As of 14 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2411.19027.

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

pith.paper-citation-record.v1
2411.19027 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:43:09.024878Z

measured 18 of 18 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 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

18 of 18 outbound references displayed

verified exact1
verified fuzzy9
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4403223-218f-41e2-a7a5-f7df8abf516b · outbound

This paper cites Inceptionnext: When inception meets convnext,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Inceptionnext: When inception meets convnext,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.373512Z

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.

Observation a0307106-c571-4591-9d39-c823d59f29ff · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations LLaMA: Open and Efficient Foundation Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:08.945708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e07aa550-8d89-4868-b50e-08aa6b7bce11 · outbound

This paper cites YOLOv10: Real-Time End-to-End Object Detection.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations YOLOv10: Real-Time End-to-End Object Detection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:08.950220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2490777-4917-45e2-a765-fdc02b71a438 · outbound

This paper cites Language Models are Few-Shot Learners.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Language Models are Few-Shot Learners

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:08.954919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 946ce818-ba41-4169-ac67-c210e899ff36 · outbound

This paper cites Defect analysis and cost- effective resilience architecture for future dram devices,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Defect analysis and cost- effective resilience architecture for future dram devices,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.362083Z

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.

Observation 52bbaeb7-fc4c-42be-b016-534758698584 · outbound

This paper cites Silent Data Corruptions at Scale.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Silent Data Corruptions at Scale

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:08.965132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6233f5da-0ba6-49fb-b127-3b1aecdbdbd2 · outbound

This paper cites Recent progress in the voltage-controlled magnetic anisotropy effect and the challenges faced in developing voltage-torque MRAM,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Recent progress in the voltage-controlled magnetic anisotropy effect and the challenges faced in developing voltage-torque MRAM,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.351358Z

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.

Observation 35a386ba-8bdc-4dea-83df-c899f7eb77b5 · outbound

This paper cites Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.340231Z

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.

Observation ad764d07-bf72-4f1a-a33d-ba9dd50cf4c5 · outbound

This paper cites Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Ft-clipact: Resilience analysis of deep neural networks and improving their fault tolerance using clipped activation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.328532Z

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.

Observation 5f924341-e733-4f4f-b5ed-ffc730dfa54c · outbound

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

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations A low-cost fault corrector for deep neural networks through range restriction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.317188Z

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.

Observation 0da94d97-b992-4caa-8e55-1b3aded779cb · outbound

This paper cites Fitact: Error resilient deep neural networks via fine-grained post-trainable activation functions,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Fitact: Error resilient deep neural networks via fine-grained post-trainable activation functions,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.305301Z

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.

Observation 900a25b8-ba6d-4140-aee2-8d30242ddf24 · outbound

This paper cites Proact: Progressive training for hybrid clipped activation function to enhance resilience of dnns,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Proact: Progressive training for hybrid clipped activation function to enhance resilience of dnns,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-12T10:43:09.207104Z

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.

Observation 3cbaa45f-a01c-43be-b455-b46cb2e09e18 · outbound

This paper cites Ares: A framework for quantifying the resilience of deep neural networks,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Ares: A framework for quantifying the resilience of deep neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.294256Z

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.

Observation f32a8765-4bb2-4702-a6b4-0b356968471c · outbound

This paper cites Weight compander: A simple weight reparameterization for regularization,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Weight compander: A simple weight reparameterization for regularization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:43:09.282835Z

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.

Observation ccbd03d1-0b88-42b2-9518-031db73934c7 · outbound

This paper cites Torchvision: Pytorch’s computer vision library,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Torchvision: Pytorch’s computer vision library,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:09.010162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f2d3171-4011-4e37-91c4-b4e5c0010a7a · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Imagenet: A large-scale hierarchical image database,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:09.014870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 384d9699-5255-4ffe-a460-d39fa675ff08 · outbound

This paper cites Deep residual learning for image recognition,.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Deep residual learning for image recognition,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:09.020199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c44e313b-44b9-4d85-bcda-f044d51e6c6f · outbound

This paper cites Decoupled Weight Decay Regularization.

Enhancing Neural Network Robustness Against Fault Injection Through Non-linear Weight Transformations Decoupled Weight Decay Regularization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T10:43:09.024878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.