LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks | Paper Citation Record

Observation 77592c3c-4b09-4466-b71f-d54242ab6f7c · outbound

This paper cites Angelopoulos and Stephen Bates.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Angelopoulos and Stephen Bates

Reference 1

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This paper cites Sorting Out Lipschitz Function Approximation.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Sorting Out Lipschitz Function Approximation

Reference 2

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Observation 6f589c42-d0be-4586-8c10-c8b4b1348f00 · outbound

This paper cites A unified algebraic perspective on lipschitz neural networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks A unified algebraic perspective on lipschitz neural networks

Reference 3

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Observation 5a33cd1b-9b22-4d70-9b4f-4d8c87625007 · outbound

This paper cites Spectrally-normalized margin bounds for neural networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Spectrally-normalized margin bounds for neural networks

Reference 4

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Observation 872dc832-08a2-404c-a0c4-4ba2b708b040 · outbound

This paper cites An adaptive orthogonal convolution scheme for efficient and flexible cnn architectures.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks An adaptive orthogonal convolution scheme for efficient and flexible cnn architectures

Reference 5

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Observation 97aa29f3-9b99-44ee-9651-5413595e161b · outbound

This paper cites Boyd and Lieven Vandenberghe.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Boyd and Lieven Vandenberghe

Reference 6

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Observation 526cf844-a02b-40a2-a5bd-5019b3852fe6 · outbound

This paper cites Verification of forecasts expressed in terms of probability.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Verification of forecasts expressed in terms of probability

Reference 7

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Observation 86698363-22e4-4bd6-8dc8-d2b4201de6b0 · outbound

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

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Pay attention to your loss : understanding misconceptions about Lipschitz neural networks

Reference 8

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Observation 6dc6bfea-337d-4b43-a572-d802ddf589b2 · outbound

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

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 9

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Observation 4bce41c4-ab00-49f0-a766-3c07d60f5cf0 · outbound

This paper cites The Road Less Scheduled.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks The Road Less Scheduled

Reference 10

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Observation 660881bc-5b86-4a30-9049-a223ac1986e7 · outbound

This paper cites In: Proceedings of the 3rd Innovations in Theoretica l Computer Science Conference On - ITCS ’12, pp.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks In: Proceedings of the 3rd Innovations in Theoretica l Computer Science Conference On - ITCS ’12, pp

Reference 11

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This paper cites Disrupting Deep Uncertainty Estimation Without Harming Accuracy.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Disrupting Deep Uncertainty Estimation Without Harming Accuracy

Reference 12

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Observation db019c5c-3649-45d1-a59e-b8fa14625dba · outbound

This paper cites Size-independent sample complexity of neural networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Size-independent sample complexity of neural networks

Reference 13

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Observation 8ed3461f-dde8-417f-b3f0-8c2f77635d01 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Explaining and Harnessing Adversarial Examples

Reference 14

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Observation 85e42d81-f3a3-4090-a628-fdea204f8eb2 · outbound

This paper cites Robust Models are less Over-Confident.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Robust Models are less Over-Confident

Reference 15

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Observation f2cc6f67-95bb-46d9-acfe-424729de7cd6 · outbound

This paper cites The Limitations of Model Uncertainty in Adversarial Settings.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks The Limitations of Model Uncertainty in Adversarial Settings

Reference 16

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Observation b4868f2c-d135-4f6b-9d61-aa850153b393 · outbound

This paper cites Weinberger.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Weinberger

Reference 17

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This paper cites Equality of Opportunity in Supervised Learning.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Equality of Opportunity in Supervised Learning

Reference 18

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This paper cites Improving model calibration with accuracy versus uncertainty optimization.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Improving model calibration with accuracy versus uncertainty optimization

Reference 19

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This paper cites Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration

Reference 20

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Observation fce9928d-d794-4054-9b46-182b678a0591 · outbound

This paper cites Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings

Reference 21

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Observation 7e4ef224-c413-45f4-833e-34744556bdbd · outbound

This paper cites Enhancing certified robustness via block reflector orthogonal layers and logit annealing loss.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Enhancing certified robustness via block reflector orthogonal layers and logit annealing loss

Reference 22

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Observation 55634e74-0b28-4f46-a777-59a101569899 · outbound

This paper cites On the Robustness of Adversarial Training Against Uncertainty Attacks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks On the Robustness of Adversarial Training Against Uncertainty Attacks

Reference 23

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This paper cites Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

Reference 24

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Observation 04f1fce1-db61-4f18-b0f5-7382f144f158 · outbound

This paper cites The devil is in the margin: Margin-based label smoothing for network calibration.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks The devil is in the margin: Margin-based label smoothing for network calibration

Reference 25

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This paper cites A Unified Approach to Interpreting Model Predictions.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks A Unified Approach to Interpreting Model Predictions

Reference 26

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Observation 8d1e7e4c-7958-4787-8dda-ef9455803a21 · outbound

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

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 27

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This paper cites Delattre, Alexandre Araujo, and Alexandre Allauzen.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Delattre, Alexandre Araujo, and Alexandre Allauzen

Reference 28

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This paper cites Spectral Normalization for Generative Adversarial Networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Spectral Normalization for Generative Adversarial Networks

Reference 29

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This paper cites Calibrating Deep Neural Networks using Focal Loss.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Calibrating Deep Neural Networks using Focal Loss

Reference 30

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Observation b093b555-99c2-4602-a29f-9cd71a08ec85 · outbound

This paper cites Murphy and Robert L.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Murphy and Robert L

Reference 31

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Observation b82a6469-0295-483b-8d91-9eeecff06f63 · outbound

This paper cites an unresolved cited work.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Unresolved cited work

Reference 32

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Observation 43ce88ba-41cf-44f5-90e8-4421a39831d4 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Obtaining well calibrated probabilities using bayesian binning

Reference 33

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This paper cites A pac-bayesian approach to spectrally-normalized margin bounds for neural networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks A pac-bayesian approach to spectrally-normalized margin bounds for neural networks

Reference 34

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This paper cites Calibration Attacks : A Comprehensive Study of Adversarial Attacks on Model Confidence.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Calibration Attacks : A Comprehensive Study of Adversarial Attacks on Model Confidence

Reference 35

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Observation ce0d5f10-8687-440e-b38f-307103e17f60 · outbound

This paper cites Almost-orthogonal layers for efficient general-purpose lipschitz networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Almost-orthogonal layers for efficient general-purpose lipschitz networks

Reference 36

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Observation afa025a8-855e-4354-97f4-3ab466670ab1 · outbound

This paper cites Intriguing properties of robust classification.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Intriguing properties of robust classification

Reference 37

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Observation 3f69bcb0-9d59-4c4d-889e-cc5f13d01a70 · outbound

This paper cites Improving Calibration through the Relationship with Adversarial Robustness.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Improving Calibration through the Relationship with Adversarial Robustness

Reference 38

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Observation 3153374d-5358-4694-a5d7-905b7912826c · outbound

This paper cites Why Should I Trust You?.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Why Should I Trust You?

Reference 39

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Observation cbaa8789-d43b-4ca5-beb5-878fa8862be9 · outbound

This paper cites Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization

Reference 40

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Observation ad6c0ec8-f8b2-4679-a815-cbeb76a9ae74 · outbound

This paper cites Skew Orthogonal Convolutions.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Skew Orthogonal Convolutions

Reference 41

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Observation 72de5711-3670-4491-809d-a249cdec3646 · outbound

This paper cites Intriguing properties of neural networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Intriguing properties of neural networks

Reference 42

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Observation 6d84b282-d232-47f2-bd99-88ff249730a4 · outbound

This paper cites Dual Focal Loss for Calibration.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Dual Focal Loss for Calibration

Reference 43

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Observation 52ce542f-be7f-43ac-84c7-e9aba7729bd5 · outbound

This paper cites Orthogonalizing convolutional layers with the cayley transform.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Orthogonalizing convolutional layers with the cayley transform

Reference 44

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Observation fb26252b-6062-48e3-a342-06886431a2ea · outbound

This paper cites Lipschitz- Margin Training : Scalable Certification of Perturbation Invariance for Deep Neural Networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Lipschitz- Margin Training : Scalable Certification of Perturbation Invariance for Deep Neural Networks

Reference 45

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Observation e0266068-18e8-4365-9776-9e330cff6305 · outbound

This paper cites Calibration of Neural Networks.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Calibration of Neural Networks

Reference 46

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Observation 2935666f-18af-4d0f-a2fa-57521a33d391 · outbound

This paper cites Sauer, Tom Hendriks, Olivier W.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Sauer, Tom Hendriks, Olivier W

Reference 47

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Observation ad873d63-c21e-45ad-b222-7dcb67126ccf · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Lipschitz regularity of deep neural networks: analysis and efficient estimation

Reference 48

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Observation 4eaea99a-8db8-48a3-9dd2-93a1a596c26a · outbound

This paper cites Algorithmic Learning in a Random World.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Algorithmic Learning in a Random World

Reference 49

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Observation 767bca4c-2d3e-4244-91ca-287148583e4f · outbound

This paper cites LOT : Layer -wise Orthogonal Training on Improving l2 Certified Robustness.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks LOT : Layer -wise Orthogonal Training on Improving l2 Certified Robustness

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T19:57:34.344395Z

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Observation f5ef9294-c872-4d63-b84f-b7d06a732a99 · outbound

This paper cites Mitigating Transformer Overconfidence via Lipschitz Regularization.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Mitigating Transformer Overconfidence via Lipschitz Regularization

Reference 51

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Observation 83db5484-5440-4e29-9ae8-44eeb46e6851 · outbound

This paper cites Obtaining calibrated probability estimates from decision trees and naive Bayesian classiﬁers.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Obtaining calibrated probability estimates from decision trees and naive Bayesian classiﬁers

Reference 52

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Observation 762cf0a1-93c2-4b3a-925e-0799cce274d6 · outbound

This paper cites Transforming classifier scores into accurate multiclass probability estimates.

LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks Transforming classifier scores into accurate multiclass probability estimates

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-10T19:57:33.819047Z

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