Adversarially Robust Spiking Neural Networks with Sparse Connectivity | Paper Citation Record

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This paper cites Intriguing properties of neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Intriguing properties of neural networks

Reference 1

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

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 2

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This paper cites Adversarial Robustness May Be at Odds With Simplicity.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Adversarial Robustness May Be at Odds With Simplicity

Reference 3

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This paper cites Adversarial robustness vs.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Adversarial robustness vs

Reference 4

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This paper cites Hydra: Pruning adversarially robust neural networks.Advances in Neural Information Processing Systems, 33, 2020.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Hydra: Pruning adversarially robust neural networks.Advances in Neural Information Processing Systems, 33, 2020

Reference 5

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This paper cites Holistic adversarially robust pruning.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Holistic adversarially robust pruning

Reference 6

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This paper cites Hire-snn: Harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Hire-snn: Harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise

Reference 7

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This paper cites SNN-RAT: Robustness en- hanced spiking neural network through regularized adversarial training.Advances in Neural Information Processing Systems, 35:24780–24793, 2022.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity SNN-RAT: Robustness en- hanced spiking neural network through regularized adversarial training.Advances in Neural Information Processing Systems, 35:24780–24793, 2022

Reference 8

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This paper cites Enhancingtherobustnessofspiking neural networks with stochastic gating mechanisms.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Enhancingtherobustnessofspiking neural networks with stochastic gating mechanisms

Reference 9

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This paper cites Enhancing adversarial robustness in SNNs with sparse gradients.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Enhancing adversarial robustness in SNNs with sparse gradients

Reference 10

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This paper cites Transactions on Machine Learning Research, 2024.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Transactions on Machine Learning Research, 2024

Reference 11

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This paper cites Towards spike-based machine intelligence with neuromorphic computing.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Towards spike-based machine intelligence with neuromorphic computing

Reference 12

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This paper cites Advancing neuromorphic com- puting with Loihi: A survey of results and outlook.Proceedings of the IEEE, 109(5):911–934, 2021.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Advancing neuromorphic com- puting with Loihi: A survey of results and outlook.Proceedings of the IEEE, 109(5):911–934, 2021

Reference 13

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This paper cites Spatio-temporalbackpropagationfor training high-performance spiking neural networks.Frontiers in Neuroscience, 12:331, 2018.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Spatio-temporalbackpropagationfor training high-performance spiking neural networks.Frontiers in Neuroscience, 12:331, 2018

Reference 14

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This paper cites Direct training for spik- ing neural networks: Faster, larger, better.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Direct training for spik- ing neural networks: Faster, larger, better

Reference 15

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This paper cites Neural Networks, 10(9):1659–1671, 1997.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Neural Networks, 10(9):1659–1671, 1997

Reference 16

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This paper cites Enabling spike-based backpropagation for training deep neural network archi- tectures.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Enabling spike-based backpropagation for training deep neural network archi- tectures

Reference 17

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This paper cites Surrogate gradient learning in spiking neural networks: Bringing the powerofgradient-basedoptimizationtospikingneuralnetworks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Surrogate gradient learning in spiking neural networks: Bringing the powerofgradient-basedoptimizationtospikingneuralnetworks

Reference 18

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This paper cites Fast-classifying, high-accuracy spiking deep networks through weight and threshold balanc- ing.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Fast-classifying, high-accuracy spiking deep networks through weight and threshold balanc- ing

Reference 19

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This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Conversion of continuous-valued deep networks to efficient event-driven networks for image classification

Reference 20

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This paper cites Going deeper in spikingneuralnetworks: Vggandresidualarchitectures.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Going deeper in spikingneuralnetworks: Vggandresidualarchitectures

Reference 21

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This paper cites Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Reference 22

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This paper cites Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization.IEEE Transactions on Neural Networks and Learning Systems, 2021.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization.IEEE Transactions on Neural Networks and Learning Systems, 2021

Reference 23

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This paper cites Computing’s energy problem (and what we can do about it).

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Computing’s energy problem (and what we can do about it)

Reference 24

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This paper cites Explaining and Harnessing Adversarial Examples.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Explaining and Harnessing Adversarial Examples

Reference 25

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This paper cites Theoreticallyprincipledtrade-offbetweenrobustnessandaccuracy.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Theoreticallyprincipledtrade-offbetweenrobustnessandaccuracy

Reference 26

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This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Improving adversarial robustness requires revisiting misclassified examples

Reference 27

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This paper cites Consistencyregularizationforadversarialrobustness.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Consistencyregularizationforadversarialrobustness

Reference 28

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This paper cites Inherent adversarial ro- bustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Inherent adversarial ro- bustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations

Reference 29

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This paper cites Towardrobustspikingneuralnetwork against adversarial perturbation.Advances in Neural Information Processing Systems, 35, 2022.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Towardrobustspikingneuralnetwork against adversarial perturbation.Advances in Neural Information Processing Systems, 35, 2022

Reference 30

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This paper cites A comprehensive analysis on adversarial robustness of spiking neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity A comprehensive analysis on adversarial robustness of spiking neural networks

Reference 31

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Observation a53eb37e-66d3-4a66-8b43-14e723fd3528 · outbound

This paper cites Is spiking secure? a comparative study on the security vul- nerabilities of spiking and deep neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Is spiking secure? a comparative study on the security vul- nerabilities of spiking and deep neural networks

Reference 32

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Observation 33db9757-b4dd-4931-9777-1f3b54cc1f65 · outbound

This paper cites Securing deep spiking neural networks against adversarial attacks through inherent structural parameters.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Securing deep spiking neural networks against adversarial attacks through inherent structural parameters

Reference 33

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Observation d7b7c45b-9f12-4bdd-8d85-0a63edd44abb · outbound

This paper cites Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

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Observation 1eeae1d8-e607-43ef-b3af-793fd424456d · outbound

This paper cites Robust stable spiking neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Robust stable spiking neural networks

Reference 35

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This paper cites Learningbothweightsandconnectionsfor efficient neural network.Advances in Neural Information Processing Systems, 28, 2015.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Learningbothweightsandconnectionsfor efficient neural network.Advances in Neural Information Processing Systems, 28, 2015

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This paper cites What’s hidden in a randomly weighted neural network? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11893–11902, 2020.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity What’s hidden in a randomly weighted neural network? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11893–11902, 2020

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This paper cites Goingdeeperwithdirectly-trained larger spiking neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Goingdeeperwithdirectly-trained larger spiking neural networks

Reference 38

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Observation 02cb5c65-49bb-4164-b2d8-01734f61ea32 · outbound

This paper cites Exploring the connection between binary and spiking neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Exploring the connection between binary and spiking neural networks

Reference 39

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Observation 1a95e3f8-197b-4a7b-af0a-84eb195e9e30 · outbound

This paper cites Unla- beleddata improvesadversarial robustness.Advancesin NeuralInformation ProcessingSystems, 32, 2019.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Unla- beleddata improvesadversarial robustness.Advancesin NeuralInformation ProcessingSystems, 32, 2019

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Observation 75099b9f-4139-4bf6-978e-002cc001575f · outbound

This paper cites Ensemble Adversarial Training: Attacks and Defenses.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Ensemble Adversarial Training: Attacks and Defenses

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This paper cites Security evaluation of pattern classifiers under attack.IEEE Transactions on Knowledge and Data Engineering, 26(4):984–996, 2013.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Security evaluation of pattern classifiers under attack.IEEE Transactions on Knowledge and Data Engineering, 26(4):984–996, 2013

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Observation ba2664b7-4711-48f5-87d2-11adeb6e27ac · outbound

This paper cites On Evaluating Adversarial Robustness.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity On Evaluating Adversarial Robustness

Reference 43

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This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

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This paper cites Rate gradient approximation attack threats deep spiking neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Rate gradient approximation attack threats deep spiking neural networks

Reference 45

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Observation 68e5b9aa-12ea-4268-9476-d3deed0fc004 · outbound

This paper cites Square attack: aquery-efficientblack-boxadversarialattackviarandomsearch.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Square attack: aquery-efficientblack-boxadversarialattackviarandomsearch

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Observation 2be19e34-94de-4870-b517-7f95ae9d4e3f · outbound

This paper cites Synaptic interaction penalty: Appropriate penalty term for energy-efficient spiking neural networks.Transactions on Machine Learning Research, 2023.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Synaptic interaction penalty: Appropriate penalty term for energy-efficient spiking neural networks.Transactions on Machine Learning Research, 2023

Reference 47

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Observation 21b8276c-a3e2-4672-99c9-b00ae4e983f2 · outbound

This paper cites Training adversarially robust sparse networks via Bayesian connectivity sampling.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Training adversarially robust sparse networks via Bayesian connectivity sampling

Reference 48

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Observation bff1e894-df23-4d29-a3a2-5eb5da7a93c4 · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Channel pruning for accelerating very deep neural networks

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Observation f4a32ffa-6dbb-40e4-917c-37dca57edfdf · outbound

This paper cites Lost in pruning: The effects of pruning neural networks beyond test accuracy.Proceedings of Machine Learning and Systems, 3:93–138, 2021.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Lost in pruning: The effects of pruning neural networks beyond test accuracy.Proceedings of Machine Learning and Systems, 3:93–138, 2021

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.992793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 319f05cf-1668-4123-99bc-b5f285ed32d5 · outbound

This paper cites Towards energy efficient spiking neural networks: An unstructured pruning framework.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Towards energy efficient spiking neural networks: An unstructured pruning framework

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.982888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7dab1b8-f64a-487f-b39e-01e8773562b5 · outbound

This paper cites Workload-balanced pruning for sparse spiking neural networks.IEEE Transactions on Emerging Topics in Computational Intelligence, 2024.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Workload-balanced pruning for sparse spiking neural networks.IEEE Transactions on Emerging Topics in Computational Intelligence, 2024

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.972092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4915d4ef-c510-4a54-aa1c-5407cbd907f5 · outbound

This paper cites Dvs-attacks: Adversarial attacks on dynamic vision sensors for spiking neural net- works.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Dvs-attacks: Adversarial attacks on dynamic vision sensors for spiking neural net- works

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.961079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 408ed891-24f3-474b-a0a6-267ddf152f89 · outbound

This paper cites Ex- ploring adversarial attack in spiking neural networks with spike-compatible gradient.IEEE Transactions on Neural Networks and Learning Systems, 2021.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Ex- ploring adversarial attack in spiking neural networks with spike-compatible gradient.IEEE Transactions on Neural Networks and Learning Systems, 2021

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.951106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d351d288-8738-4ec7-a244-32ac26f4945c · outbound

This paper cites Adversarial at- tacks on spiking convolutional neural networks for event-based vision.Frontiers in Neuro- science, 16, 2022.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Adversarial at- tacks on spiking convolutional neural networks for event-based vision.Frontiers in Neuro- science, 16, 2022

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.940413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68d8a940-0756-4a40-be0f-db2b017a82fb · outbound

This paper cites Learning multiple layers of features from tiny images.Technical Report, Uni- versity of Toronto, 2009.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Learning multiple layers of features from tiny images.Technical Report, Uni- versity of Toronto, 2009

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.930282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ab6a834-2dbe-450b-bad2-62de3d22aea3 · outbound

This paper cites Tiny ImageNet visual recognition challenge, 2015.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Tiny ImageNet visual recognition challenge, 2015

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.920167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7bbe1e5a-57df-4573-a108-1526897c2a14 · outbound

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

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Very deep convolutional networks for large-scale image recognition

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:42.732767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b968578-c7b7-44e1-8a7f-d2033eb1f91c · outbound

This paper cites Wide Residual Networks.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Wide Residual Networks

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:42.736078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c90cfb08-dd2d-4d0b-890e-cfaa1b021a85 · outbound

This paper cites Long short-term memory and learning-to-learn in networks of spiking neurons.Advances in Neural Information Processing Systems, 31, 2018.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Long short-term memory and learning-to-learn in networks of spiking neurons.Advances in Neural Information Processing Systems, 31, 2018

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.903428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 838994bf-782c-403d-956e-06bbe2ebfe43 · outbound

This paper cites Advances in Neural Information Processing Systems, 31, 2018.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Advances in Neural Information Processing Systems, 31, 2018

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.892916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d5e44dc-e319-4d24-9068-4dd5364b806a · outbound

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

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T21:05:42.745757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 827b8610-9ae1-4b89-830f-caa0636ce72b · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Adversarially Robust Spiking Neural Networks with Sparse Connectivity Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:05:42.882448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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