Paper Citation Record · LEDGER

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

As of 15 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:1908.07116.

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

pith.paper-citation-record.v1
1908.07116 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:29:34.990371Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

23 of 23 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7558958b-093b-4b57-bb37-589b3fe27e77 · outbound

This paper cites Synthesizing Robust Adversarial Examples.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Synthesizing Robust Adversarial Examples

Reference 1

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Observation 1fb0a148-3352-4f86-9456-c8b3899d7a9d · outbound

This paper cites Towards evaluating the robustness of neural net- works.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Towards evaluating the robustness of neural net- works

Reference 3

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Observation 135e4f66-8b8e-4c26-aba5-3209dccb82a9 · outbound

This paper cites EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Reference 4

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Observation f12d26b7-b430-4982-9359-7b08ef50cf2f · outbound

This paper cites Dhillon, Kamyar Aziz- zadenesheli, Jeremy D.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Dhillon, Kamyar Aziz- zadenesheli, Jeremy D

Reference 5

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Observation de49af6e-0c62-450e-b882-80c4ebf280d7 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Explaining and Harnessing Adversarial Examples

Reference 7

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Observation d2890338-764f-446e-8123-e03fe229b0a2 · outbound

This paper cites Towards Robust Neural Networks via Random Self-ensemble.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Towards Robust Neural Networks via Random Self-ensemble

Reference 12

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Observation abdf9768-1204-4e38-babd-c1e0177bc8ff · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 13

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Observation 2d6a7045-b1bb-4011-af71-3952fd431663 · outbound

This paper cites Training neural network classiﬁers for medical decision making: The effects of imbalanced datasets on classiﬁcation performance.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Training neural network classiﬁers for medical decision making: The effects of imbalanced datasets on classiﬁcation performance

Reference 14

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Observation cbaebb95-92b1-485c-8dc8-c8b687941882 · outbound

This paper cites Is robustness the cost of accuracy?–a comprehensive study on the robust- ness of 18 deep image classiﬁcation models.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Is robustness the cost of accuracy?–a comprehensive study on the robust- ness of 18 deep image classiﬁcation models

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:29:35.184066Z

Source-reported events for the cited work

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Observation 553e4f3c-7438-4778-b3b2-2a97714cf771 · outbound

This paper cites Intriguing properties of neural networks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Intriguing properties of neural networks

Reference 18

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Observation 09709e8e-cd04-4895-bfd1-85c94eb64a24 · outbound

This paper cites Using deep learning to extract scenery infor- mation in real time spatiotemporal compressed sensing.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Using deep learning to extract scenery infor- mation in real time spatiotemporal compressed sensing

Reference 20

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Observation 36e57a19-5c51-41fd-8c22-0adafab9efac · outbound

This paper cites An admm-based universal framework for ad- versarial attacks on deep neural networks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses An admm-based universal framework for ad- versarial attacks on deep neural networks

Reference 21

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Observation 4d81573c-6561-461b-b5c9-6591c32fa456 · outbound

This paper cites Fault sneaking attack: A stealthy framework for misleading deep neural networks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Fault sneaking attack: A stealthy framework for misleading deep neural networks

Reference 22

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Observation 2cf837a8-a68a-4196-81b4-579cd6c472fc · outbound

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Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Unresolved cited work

Reference 30

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Observation 0da9b32e-e5c9-4a0c-98ba-5e79acc19e1e · outbound

This paper cites The mnist database of hand- written digits.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses The mnist database of hand- written digits

Reference 1998

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Observation 6d56cbe4-3c02-4b10-9da9-fad3b2f39adf · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neu- ral networks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Distillation as a defense to adversarial perturbations against deep neu- ral networks

Reference 2008

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Observation 7bfcd256-09be-4326-9681-721babd58e8e · outbound

This paper cites Lecun, L.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Lecun, L

Reference 2009

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Observation eb077d57-03cd-49ec-868d-3a63586f8990 · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Ensemble Adversarial Training: Attacks and Defenses

Reference 2013

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Observation 83fba3a8-1374-4579-a102-2711b483bc6d · outbound

This paper cites Learning multiple layers of features from tiny images.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Learning multiple layers of features from tiny images

Reference 2014

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Observation e5eb3dd4-7072-4b62-8ae6-3d2c8f98bb74 · outbound

This paper cites Convolutional neural network architectures for matching natural language sentences.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Convolutional neural network architectures for matching natural language sentences

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:29:35.264214Z

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Observation 3a73961b-733c-44ec-8405-f0699cd4a1ce · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Dropout: a simple way to prevent neural networks from overﬁtting

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:29:35.196883Z

Source-reported events for the cited work

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Observation 52594590-9b1e-42c2-aed7-899df2e50bd4 · outbound

This paper cites Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

Reference 2017

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Source-reported events for the cited work

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Observation d69a2a91-d532-4547-9230-dfe68e754fea · outbound

This paper cites Adversarial Reprogramming of Neural Networks.

Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Adversarial Reprogramming of Neural Networks

Reference 2018

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Source-reported events for the cited work

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Pith citing papers

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