Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:01:34.401475Z
Paper Citation Record · LEDGER
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection
As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2505.11134.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
43 of 43 outbound references displayed
- verified exact2
- verified fuzzy29
- unresolved11
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 822a7937-94c6-4d63-8a71-022541d8cffd · outbound
This paper cites Obfu scated gradients give a false sense of security: Circumventing defenses to adversarial exampl es.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Obfu scated gradients give a false sense of security: Circumventing defenses to adversarial exampl es
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:35.060751Z
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 0835e864-9d86-4516-a09c-a29a225f03fa · outbound
This paper cites High-performance large-scale image recognition without normalization.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection High-performance large-scale image recognition without normalization
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:35.045039Z
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 04dbb467-7554-4ff8-84d3-bcd2ad6210af · outbound
This paper cites Why Adversarial Training of ReLU Networks Is Difficult?.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Why Adversarial Training of ReLU Networks Is Difficult?
Reference 3
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5ee2603-6c76-44dd-ba75-e53d3ee25c40 · outbound
This paper cites Imagenet: A large- scale hierarchical image database.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Imagenet: A large- scale hierarchical image database
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.213898Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e049f257-43ad-45e6-80e1-6d97a02897d3 · outbound
This paper cites Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.219643Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7fbff9c5-1954-485f-8fa9-ab49e437500a · outbound
This paper cites Snn-rat: Robustness- enhanced spiking neural network through regularized adver sarial training.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Snn-rat: Robustness- enhanced spiking neural network through regularized adver sarial training
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:35.021844Z
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 00a9d9fe-ddb8-4aad-9aa9-5cbd30e6559c · outbound
This paper cites Robust Stable Spiking Neural Networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Robust Stable Spiking Neural Networks
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T21:01:34.555697Z
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 f96576ef-4df3-4369-916a-ce3c583e3fde · outbound
This paper cites En hancing the robustness of spiking neural networks with stochastic gating mechanisms.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection En hancing the robustness of spiking neural networks with stochastic gating mechanisms
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:35.007424Z
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 6b7a0507-7dc8-4750-8cd3-69d3d8aefd9a · outbound
This paper cites Spiking jelly: An open-source machine learning infrastructure platform for spike-based intelligence.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Spiking jelly: An open-source machine learning infrastructure platform for spike-based intelligence
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.992751Z
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 62740e29-1eb0-4852-9c58-3ed016d95835 · outbound
This paper cites Deep residual learning in spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Deep residual learning in spiking neural networks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.980715Z
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 3d95507d-b44f-4bd8-b7e1-c5d60cc7c366 · outbound
This paper cites HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.244901Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fd36003f-8f7e-4c23-b51d-88a278253493 · outbound
This paper cites An i nvestigation into neural net opti- mization via hessian eigenvalue density.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection An i nvestigation into neural net opti- mization via hessian eigenvalue density
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.967275Z
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 1db69901-1c27-451a-9453-59fa74982b33 · outbound
This paper cites Explaining and Harnessing Adversarial Examples.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Explaining and Harnessing Adversarial Examples
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff0aaf45-bd09-4a59-a301-a7b1a17134dc · outbound
This paper cites Fully spiking neural network for legged robots.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Fully spiking neural network for legged robots
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.954146Z
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 d3f7c3ed-853d-43d1-95d7-255d990f3d4d · outbound
This paper cites an unresolved cited work.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Unresolved cited work
Reference 15
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T21:01:34.940011Z
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 8313df3b-bc58-4694-a62f-d17d20c84e42 · outbound
This paper cites Learning mult iple layers of features from tiny images.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Learning mult iple layers of features from tiny images
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.268541Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f531cf6c-ab95-4f56-920c-a9f468ffc65f · outbound
This paper cites Dsqn: Robust path plannin g of mobile robot based on deep spiking q-network.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Dsqn: Robust path plannin g of mobile robot based on deep spiking q-network
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.917584Z
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 fd4042e5-f983-4fd2-a912-b74900cdb6ca · outbound
This paper cites Hire- snn: Harnessing the inherent robustness of energy-efficient deep spiking neural network s by training with crafted input noise.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Hire- snn: Harnessing the inherent robustness of energy-efficient deep spiking neural network s by training with crafted input noise
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.902526Z
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 93af1deb-d5f2-4179-b50a-771848dac249 · outbound
This paper cites Ssefusion: Salient semantic enhancement for multimodal medical image fusion with mamba and dynamic spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Ssefusion: Salient semantic enhancement for multimodal medical image fusion with mamba and dynamic spiking neural networks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.888715Z
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 c2c81eba-c888-463e-8cad-bd259bb91683 · outbound
This paper cites Energy-effi cient distributed spiking neural network for wireless edge intelligence.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Energy-effi cient distributed spiking neural network for wireless edge intelligence
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.875742Z
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 1e1a6b56-2c99-4519-8b18-06930e6e6872 · outbound
This paper cites Enhancing Adversarial Robustness in SNNs with Sparse Gradients.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Enhancing Adversarial Robustness in SNNs with Sparse Gradients
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T21:01:34.502130Z
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 7cec095a-78ee-48ef-928e-97f223861cae · outbound
This paper cites Networks of spiking neurons: the third generation of neural network models.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Networks of spiking neurons: the third generation of neural network models
Reference 22
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e8a0efb-96c3-4a9a-80f0-112de100ea1b · outbound
This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Towards Deep Learning Models Resistant to Adversarial Attacks
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.302139Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92875b48-f3c3-470b-8e60-46a87f266071 · outbound
This paper cites To- wards memory-and time-efficient backpropagation for train ing spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection To- wards memory-and time-efficient backpropagation for train ing spiking neural networks
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.853618Z
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 d553e86e-91f8-48f2-9d37-b1bbcafdbd52 · outbound
This paper cites Introductory lectures on convex optimization: A basic cour se, volume 87.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Introductory lectures on convex optimization: A basic cour se, volume 87
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.839229Z
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 54717595-2c0f-4851-ae99-afe46e1e1d0c · outbound
This paper cites EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:01:34.316354Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eeed24d6-2433-4ad2-b4ae-3b7d3076a9f1 · outbound
This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection PyTorch: An Imperative Style, High-Performance Deep Learning Library
Reference 27
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ead6ff5a-2c1f-48e9-8b7e-4461329d7587 · outbound
This paper cites Towards artificial general int elligence with hybrid tianjic chip architecture.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Towards artificial general int elligence with hybrid tianjic chip architecture
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.824554Z
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 216b02cf-fe89-4495-a124-6b41f44b5969 · outbound
This paper cites Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.812087Z
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 24b269d3-aa16-4f1d-9b00-05c270e3402b · outbound
This paper cites Lid ar-driven spiking neural network for collision avoidance in autonomous driving.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Lid ar-driven spiking neural network for collision avoidance in autonomous driving
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.799355Z
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 d3d28983-f9d4-49f2-88c8-a50fd010433a · outbound
This paper cites A comprehensive analysis on adversari al robustness of spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection A comprehensive analysis on adversari al robustness of spiking neural networks
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.786302Z
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 720a2adb-97ec-4ca3-ba42-c4731244786b · outbound
This paper cites Inherent adversarial ro- bustness of deep spiking neural networks: Effects of discre te input encoding and non-linear activations.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Inherent adversarial ro- bustness of deep spiking neural networks: Effects of discre te input encoding and non-linear activations
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.773227Z
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 40ad367d-d521-49ea-a21c-789d486975fe · outbound
This paper cites Carsnn: An efficient spiking neural network for event-based autonomous cars on the loihi neuromorphic research processor.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Carsnn: An efficient spiking neural network for event-based autonomous cars on the loihi neuromorphic research processor
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.759459Z
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 df936788-bf14-4238-9b1a-d2c3b066cc19 · outbound
This paper cites Ssf: Accelerating training of spiking neural networ ks with stabilized spiking flow.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Ssf: Accelerating training of spiking neural networ ks with stabilized spiking flow
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.742577Z
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 f502ec03-d67e-4a17-b049-d5ae18165959 · outbound
This paper cites Spa tio-temporal backpropagation for training high-performance spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Spa tio-temporal backpropagation for training high-performance spiking neural networks
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.729255Z
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 cea73d1e-a21c-4c98-80d9-68cce9396551 · outbound
This paper cites Online training through time for spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Online training through time for spiking neural networks
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.713336Z
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 5ded862d-bf54-4f26-8697-09ab278b1aaf · outbound
This paper cites Feel-snn: Robust spiking neural networks with frequency encoding and evolutionary l eak factor.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Feel-snn: Robust spiking neural networks with frequency encoding and evolutionary l eak factor
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.698593Z
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 f8d34ae4-0310-4aff-ad8d-ef05ae7280ab · outbound
This paper cites Hierarchical spiking-based model for efficient image class ification with enhanced feature ex- traction and encoding.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Hierarchical spiking-based model for efficient image class ification with enhanced feature ex- traction and encoding
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.682058Z
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 0d2a83b6-5c7f-4361-8851-35c4fb736a37 · outbound
This paper cites Pyhessian: Neural networks through the lens of the hessian.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Pyhessian: Neural networks through the lens of the hessian
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.668268Z
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 c33da9fa-a343-49a2-9612-ef9d8f952932 · outbound
This paper cites Spiking neu ral networks in intelligent edge computing.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Spiking neu ral networks in intelligent edge computing
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.654814Z
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 51a59173-4d33-495c-b5e6-ea9a703df4dc · outbound
This paper cites Autonomous driving with spiking neural networks.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Autonomous driving with spiking neural networks
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.640581Z
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 c025bac3-6ecc-4836-841b-da057d4d76df · outbound
This paper cites Defending batch-level label inference and repla cement attacks in vertical federated learning.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Defending batch-level label inference and repla cement attacks in vertical federated learning
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:01:34.623861Z
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 fe58af80-4d7c-4e01-b409-72f10382f525 · outbound
This paper cites Table 5: Hyperparameter settings for experiments.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection Table 5: Hyperparameter settings for experiments
Reference 43
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-15T21:01:34.609895Z
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.
Pith citing papers
No inbound Pith citation observations are available.