Paper Citation Record · LEDGER

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss

As of 23 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2505.15174.

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

pith.paper-citation-record.v1
2505.15174 v2

Coverage vector

measured 69 of 69 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation 154c7a92-f2e7-4efa-82ae-bb7059421250 · outbound

This paper cites write newline.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss write newline

Reference 1

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This paper cites M., Malgouyres, F., and Mamalet, F.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss M., Malgouyres, F., and Mamalet, F

Reference 2

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This paper cites Sorting out L ipschitz function approximation.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Sorting out L ipschitz function approximation

Reference 3

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This paper cites J., Delattre, B., Allauzen, A., and Hu, B.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss J., Delattre, B., Allauzen, A., and Hu, B

Reference 4

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This paper cites L., Foster, D.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss L., Foster, D

Reference 5

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This paper cites Pay attention to your loss: understanding misconceptions about Lipschitz neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Pay attention to your loss: understanding misconceptions about Lipschitz neural networks

Reference 6

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This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 7

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This paper cites and Wagner, D.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Wagner, D

Reference 8

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This paper cites Robust overfitting may be mitigated by properly learned smoothening.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Robust overfitting may be mitigated by properly learned smoothening

Reference 9

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This paper cites Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU).

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)

Reference 10

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Observation a894afcc-d482-4305-93d0-ddaabd6bf2a6 · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Certified adversarial robustness via randomized smoothing

Reference 11

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This paper cites and Hein, M.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Hein, M

Reference 12

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This paper cites E., and Chau, D.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss E., and Chau, D

Reference 13

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This paper cites A new formulation of the hypermatrix householder- QR decomposition.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss A new formulation of the hypermatrix householder- QR decomposition

Reference 14

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This paper cites Incorporating Nesterov momentum into Adam.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Incorporating Nesterov momentum into Adam

Reference 15

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This paper cites Formal verification of piece-wise linear feed-forward neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Formal verification of piece-wise linear feed-forward neural networks

Reference 16

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Observation ab69fecf-6487-4fcc-85b2-ae15bdca06c6 · outbound

This paper cites Evaluating and Understanding the Robustness of Adversarial Logit Pairing.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Evaluating and Understanding the Robustness of Adversarial Logit Pairing

Reference 17

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This paper cites Generalizable adversarial training via spectral normalization.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Generalizable adversarial training via spectral normalization

Reference 18

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This paper cites Efficient and accurate estimation of Lipschitz constants for deep neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Efficient and accurate estimation of Lipschitz constants for deep neural networks

Reference 19

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This paper cites Explaining and Harnessing Adversarial Examples.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Explaining and Harnessing Adversarial Examples

Reference 20

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This paper cites On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

Reference 21

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 22

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This paper cites J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al

Reference 23

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This paper cites Unlocking deterministic robustness certification on Imagenet.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unlocking deterministic robustness certification on Imagenet

Reference 24

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This paper cites A recipe for improved certifiable robustness.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss A recipe for improved certifiable robustness

Reference 25

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unresolved cited work

Reference 26

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This paper cites Elucidating the design space of diffusion-based generative models.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Elucidating the design space of diffusion-based generative models

Reference 27

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Guiding a diffusion model with a bad version of itself

Reference 28

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Analyzing and improving the training dynamics of diffusion models

Reference 29

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This paper cites Certified robustness to adversarial examples with differential privacy.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Certified robustness to adversarial examples with differential privacy

Reference 30

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Talagrand, M

Reference 31

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Kim, D

Reference 32

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Globally-robust neural networks

Reference 33

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This paper cites Sok: Certified robustness for deep neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Sok: Certified robustness for deep neural networks

Reference 34

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss B., and Jacobsen, J.-H

Reference 35

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This paper cites Focal loss for dense object detection.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Focal loss for dense object detection

Reference 36

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Towards deep learning models resistant to adversarial attacks

Reference 37

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This paper cites Connecting certified and adversarial training.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Connecting certified and adversarial training

Reference 38

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This paper cites J., Araujo, A., and Allauzen, A.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss J., Araujo, A., and Allauzen, A

Reference 39

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Foundations of machine learning

Reference 40

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss N., Eckert, F., Fischer, M., and Vechev, M

Reference 41

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Orthogonal Wasserstein GANs

Reference 42

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Pytorch: An imperative style, high-performance deep learning library

Reference 43

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Lampert, C

Reference 44

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This paper cites 1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss 1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness

Reference 45

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Deep isometric learning for visual recognition

Reference 46

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This paper cites Defense-gan: Protecting classifiers against adversarial attacks using generative models.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Defense-gan: Protecting classifiers against adversarial attacks using generative models

Reference 47

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Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unresolved cited work

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This paper cites and Parlett, B.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Parlett, B

Reference 49

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7cb7722e-cd07-433d-9570-383d9512bc64 · outbound

This paper cites A., Xu, Z., Dickerson, J., Studer, C., Davis, L.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss A., Xu, Z., Dickerson, J., Studer, C., Davis, L

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.857751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation beedbd2c-0b3a-417b-9b20-36b8ca65f8b6 · outbound

This paper cites Z., and Hsieh, C.-J.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Z., and Hsieh, C.-J

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.749591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d3d76f20-b1f4-499b-aec0-b4ab4d862d23 · outbound

This paper cites and Feizi, S.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Feizi, S

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.622437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1eb0f4bb-e3c2-4c26-871a-39d8fdc98da4 · outbound

This paper cites and Feizi, S.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Feizi, S

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.449774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e5941ead-247f-458f-a660-7bd0de89d281 · outbound

This paper cites and Feizi, S.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Feizi, S

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.301613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf9f20c4-7a15-4096-ba5c-2d80a25c8720 · outbound

This paper cites Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.156468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c7ee87b-ff6a-47e2-964a-1b7d263663de · outbound

This paper cites Intriguing properties of neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Intriguing properties of neural networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:32:22.378569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b3267cb-c5a7-4db7-998e-f7b379343d21 · outbound

This paper cites and Kolter, J.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Kolter, J

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:27.015712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e1d2fadf-5cef-48df-a9b6-c271a97b5d93 · outbound

This paper cites Lipschitz -margin training: Scalable certification of perturbation invariance for deep neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Lipschitz -margin training: Scalable certification of perturbation invariance for deep neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:26.862906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8146525b-66aa-4c0e-b890-10716557554e · outbound

This paper cites and Scaman, K.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Scaman, K

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:26.724646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a99eadda-4822-4b94-8fa1-a7a0450538f4 · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:32:23.068781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93504260-5e0e-42f4-a66a-95fdcd7ebf2d · outbound

This paper cites and Manchester, I.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss and Manchester, I

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:26.582750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d0165e8c-3b71-4926-8286-4ea817cfa227 · outbound

This paper cites an unresolved cited work.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:32:26.428468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d1edbcbe-0905-429e-86cd-a195dc37b431 · outbound

This paper cites Better diffusion models further improve adversarial training.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Better diffusion models further improve adversarial training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:26.202096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ba1c6ff-b1e4-466d-8af4-5f8f018b8b05 · outbound

This paper cites Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:26.016511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 734dd223-f2ca-4794-83f1-167af86ae056 · outbound

This paper cites LOT : Layer-wise orthogonal training on improving l2 certified robustness.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss LOT : Layer-wise orthogonal training on improving l2 certified robustness

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:25.827153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4c3444d8-f1b2-445c-8a54-6c096f347d5c · outbound

This paper cites E., Salman, H., Razenshteyn, I., and Li, J.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss E., Salman, H., Razenshteyn, I., and Li, J

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:25.641641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5c06de3b-519e-412a-bd5c-8a0d02100e7c · outbound

This paper cites Constructing orthogonal convolutions in an explicit manner.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Constructing orthogonal convolutions in an explicit manner

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:25.385951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8ea6dc5e-ca8d-4b8b-ac28-dc987074d193 · outbound

This paper cites an unresolved cited work.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:32:25.187759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dab1ec4b-f616-40cd-a883-abce9e3162e2 · outbound

This paper cites an unresolved cited work.

Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:32:24.983169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 93afc093-8224-46b6-aba6-156f4d9d2114 · inbound

On the Extreme Variance of Certified Local Robustness Across Model Seeds cites this paper.

On the Extreme Variance of Certified Local Robustness Across Model Seeds Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:57:54.091626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.