Paper Citation Record · LEDGER

On Hardening DNNs against Noisy Computations

As of 11 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2501.14531.

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

pith.paper-citation-record.v1
2501.14531 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:07:02.505099Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

34 of 34 outbound references displayed

verified exact6
verified fuzzy19
unresolved9
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7bb16a81-0607-4665-9913-014818985aee · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

On Hardening DNNs against Noisy Computations Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

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Source-reported events for the cited work

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Observation 37311bef-3caf-4542-90d9-8e6e628fd8e9 · outbound

This paper cites Precise neural network computation with imprecise analog devices.

On Hardening DNNs against Noisy Computations Precise neural network computation with imprecise analog devices

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:07:02.895783Z

Source-reported events for the cited work

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Observation a4aceae1-1e0c-42b6-8153-68572bea5e28 · outbound

This paper cites Walking Noise: On Layer-Specific Robustness of Neu- ral Architectures against Noisy Computations and Asso- ciated Characteristic Learning Dynamics.

On Hardening DNNs against Noisy Computations Walking Noise: On Layer-Specific Robustness of Neu- ral Architectures against Noisy Computations and Asso- ciated Characteristic Learning Dynamics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:03.140820Z

Source-reported events for the cited work

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Observation e70940b6-f2fd-4a29-988a-8c9a7641a04a · outbound

This paper cites Probabilistic Photonic Computing with Chaotic Light.

On Hardening DNNs against Noisy Computations Probabilistic Photonic Computing with Chaotic Light

Reference 4

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Observation 5302b329-68c5-4ed6-aa02-090d6f0540f6 · outbound

This paper cites Robust quantization: One model to rule them all.

On Hardening DNNs against Noisy Computations Robust quantization: One model to rule them all

Reference 5

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Source-reported events for the cited work

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Observation a803637a-78e8-4bbe-9e89-a1f72340bdf1 · outbound

This paper cites Exploring the impact of random tele- graph noise-induced accuracy loss on resistive RAM- based deep neural network.

On Hardening DNNs against Noisy Computations Exploring the impact of random tele- graph noise-induced accuracy loss on resistive RAM- based deep neural network

Reference 6

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Source-reported events for the cited work

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Observation d8f36dab-1fa1-45be-bdc9-5cd9a677e8ac · outbound

This paper cites Relative robustness of quantized neural networks against adversarial attacks.

On Hardening DNNs against Noisy Computations Relative robustness of quantized neural networks against adversarial attacks

Reference 7

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Source-reported events for the cited work

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Observation 77015ba3-4950-426e-8d30-716cc39b1c60 · outbound

This paper cites Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification.

On Hardening DNNs against Noisy Computations Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification

Reference 8

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Source-reported events for the cited work

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Observation eb670dcc-0ad7-4ea0-bb0e-e4c48f61e1bb · outbound

This paper cites A survey of quantization methods for efficient neural network inference.

On Hardening DNNs against Noisy Computations A survey of quantization methods for efficient neural network inference

Reference 9

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Source-reported events for the cited work

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Observation ec097a0f-21bf-49b5-95b8-fe0e9f0f7ead · outbound

This paper cites How many bits does it take to quantize your neural network?.

On Hardening DNNs against Noisy Computations How many bits does it take to quantize your neural network?

Reference 10

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Source-reported events for the cited work

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Observation 7ccaadd4-1bcb-4674-a741-abb5e7c3c7d4 · outbound

This paper cites On the adversarial robustness of quantized neural net- works.

On Hardening DNNs against Noisy Computations On the adversarial robustness of quantized neural net- works

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:03.095231Z

Source-reported events for the cited work

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Observation ff986b70-7b7b-423d-9449-e428267b3719 · outbound

This paper cites Deep residual learning for image recognition.

On Hardening DNNs against Noisy Computations Deep residual learning for image recognition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:03.083663Z

Source-reported events for the cited work

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Observation dc8e1d92-b2b4-4413-9bc9-e784cfd8bd0b · outbound

This paper cites Neural network compression for noisy storage devices.

On Hardening DNNs against Noisy Computations Neural network compression for noisy storage devices

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:03.071755Z

Source-reported events for the cited work

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Observation 731ee32b-a2e9-4b36-a650-5e7432f62c01 · outbound

This paper cites Accurate deep neural network infer- ence using computational phase-change memory.

On Hardening DNNs against Noisy Computations Accurate deep neural network infer- ence using computational phase-change memory

Reference 14

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Source-reported events for the cited work

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Observation 004e58b7-a735-4d9b-82d0-3ae401694b1f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

On Hardening DNNs against Noisy Computations Adam: A Method for Stochastic Optimization

Reference 15

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Source-reported events for the cited work

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Observation 01c2b031-eefd-4e2f-8a48-fb3b15496bdb · outbound

This paper cites Towards Addressing Noise and Static Variations of Analog Computations Using Effi- cient Retraining.

On Hardening DNNs against Noisy Computations Towards Addressing Noise and Static Variations of Analog Computations Using Effi- cient Retraining

Reference 16

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Source-reported events for the cited work

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Observation e9d7c422-a846-45f0-b7e7-29a72050585f · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

On Hardening DNNs against Noisy Computations Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 17

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Observation eaed201b-688b-4c7c-8d38-b2eb72f87d1f · outbound

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On Hardening DNNs against Noisy Computations Unresolved cited work

Reference 18

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Source-reported events for the cited work

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Observation d3b6d4f4-db37-42f1-a97a-34b315cb9ce5 · outbound

This paper cites On the Non-Associativity of Analog Computations.

On Hardening DNNs against Noisy Computations On the Non-Associativity of Analog Computations

Reference 19

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Source-reported events for the cited work

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Observation 1a1b626a-6c9d-4c55-90f1-7378a77a5a2d · outbound

This paper cites Gradient-based learning applied to document recognition.

On Hardening DNNs against Noisy Computations Gradient-based learning applied to document recognition

Reference 20

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Source-reported events for the cited work

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Observation 69a4052c-4ff4-4575-9fac-bfb8ac64c49a · outbound

This paper cites Defensive Quan- tization: When Efficiency Meets Robustness.

On Hardening DNNs against Noisy Computations Defensive Quan- tization: When Efficiency Meets Robustness

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:03.013672Z

Source-reported events for the cited work

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Observation 14cdb53a-b953-43a2-addf-63ca0b99c34a · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

On Hardening DNNs against Noisy Computations SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 22

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Source-reported events for the cited work

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Observation 8aa576a3-1cd0-4591-aee7-772a95c8f367 · outbound

This paper cites Mixed-signal computing for deep neural network inference.

On Hardening DNNs against Noisy Computations Mixed-signal computing for deep neural network inference

Reference 23

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Observation 902c6d47-41e0-4a68-837e-5239f08e4271 · outbound

This paper cites A White Paper on Neural Network Quantization.

On Hardening DNNs against Noisy Computations A White Paper on Neural Network Quantization

Reference 24

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Observation f5387166-15c3-4418-991f-f71ac7d70efe · outbound

This paper cites XILINX/brevitas.

On Hardening DNNs against Noisy Computations XILINX/brevitas

Reference 25

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Source-reported events for the cited work

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Observation 932bc61b-b53a-4f77-ab4b-15df4755ea93 · outbound

This paper cites Model compression via distillation and quantization.

On Hardening DNNs against Noisy Computations Model compression via distillation and quantization

Reference 26

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Source-reported events for the cited work

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Observation 103c5167-09d7-46c7-a696-a53b9aeecbc0 · outbound

This paper cites Analog/mixed-signal hardware error modeling for deep learning inference.

On Hardening DNNs against Noisy Computations Analog/mixed-signal hardware error modeling for deep learning inference

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:02.979416Z

Source-reported events for the cited work

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Observation 77289342-5ed6-4640-809d-00cb86bb2e1b · outbound

This paper cites Resource-Efficient Neural Net- works for Embedded Systems.

On Hardening DNNs against Noisy Computations Resource-Efficient Neural Net- works for Embedded Systems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:02.968155Z

Source-reported events for the cited work

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Observation 41d92f9e-7484-4a2f-b33c-1e4a9e26aa84 · outbound

This paper cites Denoising noisy neural networks: A bayesian approach with compensation.

On Hardening DNNs against Noisy Computations Denoising noisy neural networks: A bayesian approach with compensation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:02.956078Z

Source-reported events for the cited work

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Observation 2fb8b824-7079-420b-a77a-31f40a00027a · outbound

This paper cites Deep learning with coherent nanophotonic circuits.

On Hardening DNNs against Noisy Computations Deep learning with coherent nanophotonic circuits

Reference 30

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Source-reported events for the cited work

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Observation 63025512-0c23-4a9b-a16d-f57e2eb1efa3 · outbound

This paper cites Very deep convolutional networks for large-scale image recogni- tion.

On Hardening DNNs against Noisy Computations Very deep convolutional networks for large-scale image recogni- tion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:02.930763Z

Source-reported events for the cited work

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Observation 3d6b475b-66da-4e39-9a15-f58a7d067144 · outbound

This paper cites Resiliency of Deep Neural Networks under Quantization.

On Hardening DNNs against Noisy Computations Resiliency of Deep Neural Networks under Quantization

Reference 32

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This paper cites Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach.

On Hardening DNNs against Noisy Computations Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:07:02.918314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ddefcb4d-621b-4f9f-b401-108d5f0492d6 · outbound

This paper cites Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation.

On Hardening DNNs against Noisy Computations Noisy Machines: Understanding Noisy Neural Networks and Enhancing Robustness to Analog Hardware Errors Using Distillation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:07:02.631470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.