Paper Citation Record · LEDGER

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 5 inbound Pith citation observations for arXiv:2412.07022.

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

pith.paper-citation-record.v1
2412.07022 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:15:05.534464Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:57:10.155575Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-10T15:47:23.188694Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy18
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af555d96-56a6-48ca-bcc5-a4f6237ce48a · outbound

This paper cites Wein- berger.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Wein- berger

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.817300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 26676f62-9c78-4065-a3c7-aa9a020fec1f · outbound

This paper cites Y ., Zhou, X., Lin, M., Sun, J.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Y ., Zhou, X., Lin, M., Sun, J

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.804637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3adcd821-1067-435a-b41d-d86c47673cba · outbound

This paper cites ”Ensemble methods in machine learning.” In Multiple classifier systems, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Ensemble methods in machine learning.” In Multiple classifier systems, pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.791198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ca161af-2f0c-44c8-abb0-d8d8561a7eba · outbound

This paper cites ”Simple and scalable predictive uncertainty estimation using deep en- sembles.” In Advances in neural information processing systems, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Simple and scalable predictive uncertainty estimation using deep en- sembles.” In Advances in neural information processing systems, pp

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.776640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97a248b6-577c-4d92-9147-303456bad563 · outbound

This paper cites ”Explaining the behavior of neuron activations in deep neural networks.” Ad Hoc Networks, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Explaining the behavior of neuron activations in deep neural networks.” Ad Hoc Networks, vol

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.761991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a0b24cc-12f1-4484-9abf-91f78fc43f3b · outbound

This paper cites ”DFT-Spread Based PAPR Reduction of OFDM for Short Reach Communication Systems.” In Communications, Signal Processing, and Systems, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”DFT-Spread Based PAPR Reduction of OFDM for Short Reach Communication Systems.” In Communications, Signal Processing, and Systems, pp

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.749049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95bacb7e-8afb-4c81-ae24-c63e55498bb3 · outbound

This paper cites ”Deep reinforcement learning based computation offloading for mobility-aware edge computing.” In Communications and Network- ing, 2019, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Deep reinforcement learning based computation offloading for mobility-aware edge computing.” In Communications and Network- ing, 2019, pp

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.737652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0b70dda9-099f-4db5-8a17-03c63554b2c9 · outbound

This paper cites ”Improving robustness of deep neural networks via large-difference transformation.” Neurocomputing, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Improving robustness of deep neural networks via large-difference transformation.” Neurocomputing, vol

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.725712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96e62053-f992-44f1-99d3-11b9050cd556 · outbound

This paper cites ”Partial interference alignment for heterogeneous cellular networks.” IEEE Access, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Partial interference alignment for heterogeneous cellular networks.” IEEE Access, vol

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.713263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9033789e-11ba-4f4b-a6a6-2b1e2d281eb7 · outbound

This paper cites ”Representation learning and nature encoded fusion for heterogeneous sensor networks.” IEEE Access, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Representation learning and nature encoded fusion for heterogeneous sensor networks.” IEEE Access, vol

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.702756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9246667f-ac3b-4221-a146-92709d581ba5 · outbound

This paper cites ”Performance analysis of cooperative multicell precoding with global CSI and local individual CSI in the large dimensional regime.” IEEE Transactions on Vehicular Technology, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Performance analysis of cooperative multicell precoding with global CSI and local individual CSI in the large dimensional regime.” IEEE Transactions on Vehicular Technology, vol

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.691381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ed328fa9-9e43-494b-a56e-58136c4e86ba · outbound

This paper cites ”Optimization for user centric mas- sive mimo cell free networks via large system analysis.” In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), 2016.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Optimization for user centric mas- sive mimo cell free networks via large system analysis.” In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), 2016

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.678154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 245be100-7be2-463b-96b9-c4420e028b52 · outbound

This paper cites an unresolved cited work.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:15:05.664558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bd7b3d0b-b282-49f9-95ff-617e32fbf61e · outbound

This paper cites an unresolved cited work.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:15:05.652364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e10d9859-0126-438d-aa05-66e75368d4fe · outbound

This paper cites ”Low complexity optimization for user centric cellular networks via large dimensional analysis.” Physical Communication, vol.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Low complexity optimization for user centric cellular networks via large dimensional analysis.” Physical Communication, vol

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.639321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d693af0b-baf5-400b-b7e3-433d2e38c565 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness Explaining and Harnessing Adversarial Examples

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T19:15:05.514010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09ced281-b6b5-4198-8c9e-d83f3c4956db · outbound

This paper cites ”Fully convolu- tional networks for semantic segmentation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Fully convolu- tional networks for semantic segmentation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.624701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3f2b87ff-2f62-4c43-988d-c1dee79a44b9 · outbound

This paper cites ”Improving deep learning with generic data augmentation.” In 2018 IEEE symposium series on com- putational intelligence (SSCI), pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Improving deep learning with generic data augmentation.” In 2018 IEEE symposium series on com- putational intelligence (SSCI), pp

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.612387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b4a7c080-56f2-4841-801d-6a5c24e9ac1b · outbound

This paper cites ”Group equivariant convolutional net- works.” In International conference on machine learning, pp.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Group equivariant convolutional net- works.” In International conference on machine learning, pp

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.600242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b7d3bbe-d22b-4cab-b5e5-8b2b49943058 · outbound

This paper cites ”Spatial pyramid pooling in deep convolutional networks for visual recognition.” IEEE transactions on pattern analysis and machine intelligence 37, no.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Spatial pyramid pooling in deep convolutional networks for visual recognition.” IEEE transactions on pattern analysis and machine intelligence 37, no

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.589681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4dfbaebc-4a4d-4aa0-98a7-b943552d5eaf · outbound

This paper cites ”Benchmarking neural network robustness to common corruptions and perturbations.” In Pro- ceedings of the International Conference on Learning Representations (ICLR), 2019.

Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness ”Benchmarking neural network robustness to common corruptions and perturbations.” In Pro- ceedings of the International Conference on Learning Representations (ICLR), 2019

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:15:05.576645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation d30cfbcc-753e-46a7-8295-39db115af3b4 · inbound

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning cites this paper.

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:57:10.155575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b47b93a-e0c4-4bd6-a843-269bf9195ad0 · inbound

Explainable Novel Category Discovery in Semantic Concept Space cites this paper.

Explainable Novel Category Discovery in Semantic Concept Space Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T17:32:24.547251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65310eba-0524-4ea7-98df-0a2f9476d6ff · inbound

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs cites this paper.

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-10T15:47:23.189844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72e7e636-a05c-4b82-881f-5ca7f1eb5449 · inbound

Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks cites this paper.

Learning to Transmit: Volatility-Aware Predictive Communication for Energy-Efficient IoT Networks Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-01T12:21:51.616392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4187ea7e-2c05-4ca1-aa10-17fbc28ccd8d · inbound

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI cites this paper.

Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T16:59:10.624519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.