Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation | Paper Citation Record

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This paper cites Denoising diffusion probabilistic models.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Denoising diffusion probabilistic models

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This paper cites Training language models to follow instructions with human feedback.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Training language models to follow instructions with human feedback

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This paper cites Trustworthy ai: A computational perspective.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Trustworthy ai: A computational perspective

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This paper cites Identifying and mitigating the security risks of generative ai.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Identifying and mitigating the security risks of generative ai

Reference 4

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This paper cites Generalized out-of-distribution detection: A survey.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Generalized out-of-distribution detection: A survey

Reference 5

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This paper cites Visualizing data using t-sne.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Visualizing data using t-sne

Reference 6

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This paper cites Energy-based out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Energy-based out-of-distribution detection

Reference 7

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This paper cites Wood: Wasserstein-based out-of- distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Wood: Wasserstein-based out-of- distribution detection

Reference 8

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This paper cites On the importance of gradients for detecting distributional shifts in the wild.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation On the importance of gradients for detecting distributional shifts in the wild

Reference 9

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This paper cites Low-dimensional gradient helps out-of- distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Low-dimensional gradient helps out-of- distribution detection

Reference 10

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This paper cites React: Out-of-distribution detection with rectified activations.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation React: Out-of-distribution detection with rectified activations

Reference 11

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This paper cites Out-of-distribution detection with deep nearest neighbors.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Out-of-distribution detection with deep nearest neighbors

Reference 12

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This paper cites Revisit pca-based technique for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Revisit pca-based technique for out-of-distribution detection

Reference 13

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This paper cites Boosting out-of-distribution detection with typical features.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Boosting out-of-distribution detection with typical features

Reference 14

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This paper cites Vra: variational rectified activation for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Vra: variational rectified activation for out-of-distribution detection

Reference 15

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This paper cites Extremely simple activation shaping for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Extremely simple activation shaping for out-of-distribution detection

Reference 16

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This paper cites Scaling for training time and post-hoc out-of-distribution detection enhancement.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Scaling for training time and post-hoc out-of-distribution detection enhancement

Reference 17

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This paper cites Discriminability-driven channel selection for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Discriminability-driven channel selection for out-of-distribution detection

Reference 18

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This paper cites Line: Out-of-distribution detection by leveraging important neurons.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Line: Out-of-distribution detection by leveraging important neurons

Reference 19

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This paper cites Rankfeat: Rank-1 feature removal for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Rankfeat: Rank-1 feature removal for out-of-distribution detection

Reference 20

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This paper cites A simple unified framework for detecting out-of- distribution samples and adversarial attacks.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation A simple unified framework for detecting out-of- distribution samples and adversarial attacks

Reference 21

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This paper cites Nearest neighbor guidance for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Nearest neighbor guidance for out-of-distribution detection

Reference 22

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This paper cites Out-of- distribution detection based on in-distribution data patterns memorization with modern hopfield energy.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Out-of- distribution detection based on in-distribution data patterns memorization with modern hopfield energy

Reference 23

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This paper cites Characterizing submanifold region for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Characterizing submanifold region for out-of-distribution detection

Reference 24

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This paper cites Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features

Reference 25

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Out-of-distribution detection using union of 1-dimensional subspaces

Reference 26

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This paper cites Kernel principal component analysis.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Kernel principal component analysis

Reference 27

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This paper cites Nonlinear component analysis as a kernel eigenvalue problem.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Nonlinear component analysis as a kernel eigenvalue problem

Reference 28

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This paper cites Imagenet: A large-scale hierarchical image database.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Imagenet: A large-scale hierarchical image database

Reference 29

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Unresolved cited work

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This paper cites Principal component analysis.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Principal component analysis

Reference 31

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This paper cites Random features for large-scale kernel machines.Advances in neural information processing systems, 20, 2007.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Random features for large-scale kernel machines.Advances in neural information processing systems, 20, 2007

Reference 32

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Using the nyström method to speed up kernel machines

Reference 33

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This paper cites Kernel pca for out-of- distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Kernel pca for out-of- distribution detection

Reference 34

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This paper cites Fourier analysis on groups, volume 121967.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Fourier analysis on groups, volume 121967

Reference 35

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This paper cites Learning multiple layers of features from tiny images.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Learning multiple layers of features from tiny images

Reference 36

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This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 37

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This paper cites Places: A 10 million image database for scene recognition.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Places: A 10 million image database for scene recognition

Reference 38

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Modern multidimensional scaling: Theory and applications

Reference 39

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Unresolved cited work

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Sampling methods for the nyström method

Reference 41

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Support-vector networks

Reference 42

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This paper cites Randomized nonlinear component analysis.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Randomized nonlinear component analysis

Reference 43

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This paper cites Streaming kernel pca withO(√n) random features.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Streaming kernel pca withO(√n) random features

Reference 44

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This paper cites Gain with no pain: Efficiency of kernel-pca by nyström sampling.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Gain with no pain: Efficiency of kernel-pca by nyström sampling

Reference 45

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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Kernel PCA with the Nystr\"om method

Reference 46

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This paper cites Approximate kernel pca: Computational versus statistical trade-off.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Approximate kernel pca: Computational versus statistical trade-off

Reference 47

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This paper cites Statistical optimality and computational efficiency of nystrom kernel pca.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Statistical optimality and computational efficiency of nystrom kernel pca

Reference 48

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This paper cites The pre-image problem in kernel methods.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation The pre-image problem in kernel methods

Reference 49

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This paper cites Rankfeat&rankweight: Rank-1 feature/weight removal for out-of- distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Rankfeat&rankweight: Rank-1 feature/weight removal for out-of- distribution detection

Reference 50

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This paper cites Deep residual learning for image recognition.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Deep residual learning for image recognition

Reference 51

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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 52

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 53

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This paper cites The inaturalist species classification and detection dataset.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation The inaturalist species classification and detection dataset

Reference 54

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Observation 6014d97c-7a46-4e74-a4e6-f8b7b7bf4b1e · outbound

This paper cites Sun database: Large-scale scene recognition from abbey to zoo.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Sun database: Large-scale scene recognition from abbey to zoo

Reference 55

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This paper cites Describing textures in the wild.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Describing textures in the wild

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T15:25:59.983080Z

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Observation 1408d457-d737-45cc-aa2b-21be5b0b4ed1 · outbound

This paper cites Supervised contrastive learning.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Supervised contrastive learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:00.042564Z

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Observation e35cd735-8657-4190-b6b9-b96d60eea2b9 · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation A baseline for detecting misclassified and out-of-distribution examples in neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:10.689047Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cbc967b7-5c5b-43a9-967f-90932b001955 · outbound

This paper cites Scaling out-of-distribution detection for real-world settings.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Scaling out-of-distribution detection for real-world settings

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:10.320363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aafbb686-401f-4b4a-b831-09ff6487af49 · outbound

This paper cites Enhancing the reliability of out-of-distribution image detection in neural networks.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Enhancing the reliability of out-of-distribution image detection in neural networks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:00.174736Z

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Observation 6d7c2d74-27af-4f16-84ff-f0884e61ac26 · outbound

This paper cites Vim: Out-of-distribution with virtual-logit matching.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Vim: Out-of-distribution with virtual-logit matching

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:10.058914Z

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Observation 13e020ba-5343-485d-8df3-f5f247b74975 · outbound

This paper cites Decoupling maxlogit for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Decoupling maxlogit for out-of-distribution detection

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:09.827054Z

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Observation 7f1abfee-a0b7-43ad-899a-fc900a5fd022 · outbound

This paper cites Ssd: A unified framework for self-supervised outlier detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Ssd: A unified framework for self-supervised outlier detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:09.588995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ccd598ad-31e3-4b4d-8819-f1f9944c0077 · outbound

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

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Pytorch: An imperative style, high-performance deep learning library

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:00.825941Z

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Observation 60790d4d-bc87-4d96-9abf-1bbc04c4887a · outbound

This paper cites Dice: Leveraging sparsification for out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Dice: Leveraging sparsification for out-of-distribution detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:09.338965Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e105de48-ce66-41f7-a91f-1a984f8397eb · outbound

This paper cites Gradorth: A simple yet efficient out-of-distribution detection with orthogonal projection of gradients.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Gradorth: A simple yet efficient out-of-distribution detection with orthogonal projection of gradients

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:09.019377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 25c300a3-b39c-4fab-a2f0-34db9fc8aa1f · outbound

This paper cites Billion-scale similarity search with gpus.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Billion-scale similarity search with gpus

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:01.524084Z

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Observation f79ccac1-85d3-4131-bf78-7f9bde3e4c73 · outbound

This paper cites Fast and scalable polynomial kernels via explicit feature maps.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Fast and scalable polynomial kernels via explicit feature maps

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.943850Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f0eb060d-3240-4d60-b735-3301d6998eee · outbound

This paper cites Kernel pca for novelty detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Kernel pca for novelty detection

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.753488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1ce77117-0923-4621-895e-78c299ac1c01 · outbound

This paper cites Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.507571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 117aa984-1983-42b4-b6ca-eca34fbaced3 · outbound

This paper cites Gradient-regularized out-of- distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Gradient-regularized out-of- distribution detection

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.433948Z

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Observation ecfa5ee6-26dc-4b4b-9876-017641c2a385 · outbound

This paper cites Block selection method for using feature norm in out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Block selection method for using feature norm in out-of-distribution detection

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.343350Z

Source-reported events for the cited work

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Observation c15e76a7-2174-4a41-8471-d447e08ca588 · outbound

This paper cites Wdiscood: Out-of-distribution detection via whitened linear discriminant analysis.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Wdiscood: Out-of-distribution detection via whitened linear discriminant analysis

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.191316Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b0e0088-4fea-408a-a95f-019c93f0eca9 · outbound

This paper cites Revisiting deep ensemble for out-of-distribution detection: A loss landscape perspective.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Revisiting deep ensemble for out-of-distribution detection: A loss landscape perspective

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:08.056242Z

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Observation 636ba5e8-3ca4-4666-8695-40bd61a0d434 · outbound

This paper cites Unsupervised out-of-distribution detection with diffusion inpainting.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Unsupervised out-of-distribution detection with diffusion inpainting

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.905293Z

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Observation 9ff27088-f9ec-4944-a616-1f90ff6701f7 · outbound

This paper cites Csi: Novelty detection via contrastive learning on distributionally shifted instances.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Csi: Novelty detection via contrastive learning on distributionally shifted instances

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.739289Z

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Observation fdd75a8b-c412-494f-a145-2e09c873b7dd · outbound

This paper cites Is out-of-distribution detection learnable? Advances in Neural Information Processing Systems, 35:37199–37213, 2022.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Is out-of-distribution detection learnable? Advances in Neural Information Processing Systems, 35:37199–37213, 2022

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.568365Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0601a1ac-f53f-45e6-8e64-8b2d63b20bf0 · outbound

This paper cites Provable guarantees for understanding out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Provable guarantees for understanding out-of-distribution detection

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.370229Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cdcb9762-f7c4-4682-8ae9-88c2df6051c5 · outbound

This paper cites Out-of-distribution detection by regaining lost clues.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Out-of-distribution detection by regaining lost clues

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.210703Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f5e0630b-d43e-4b5e-b6c3-1da2247b8a80 · outbound

This paper cites Supervision adaptation balancing in-distribution generalization and out-of-distribution detection.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Supervision adaptation balancing in-distribution generalization and out-of-distribution detection

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:07.078741Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6e1ff4e6-edfd-4b67-b7cc-01d8c61bde6d · outbound

This paper cites Random features for kernel approximation: A survey on algorithms, theory, and beyond.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Random features for kernel approximation: A survey on algorithms, theory, and beyond

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.937833Z

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Observation 98da6ef7-faea-497d-a747-be36bd5a1d31 · outbound

This paper cites End-to-end kernel learning via generative random fourier features.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation End-to-end kernel learning via generative random fourier features

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.770134Z

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Observation 050e12ad-6ebe-4431-a552-81115a3df26c · outbound

This paper cites Nyström method vs random fourier features: A theoretical and empirical comparison.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Nyström method vs random fourier features: A theoretical and empirical comparison

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.647443Z

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Observation 6e8bbcc3-48b8-4f47-b014-5347dd687e43 · outbound

This paper cites Reconciling modern machine-learning practice and the classical bias–variance trade-off.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Reconciling modern machine-learning practice and the classical bias–variance trade-off

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.495662Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d3ee8969-4d89-49b5-ad68-4dd8168459a2 · outbound

This paper cites Convolutional spectral kernel learning with generalization guarantees.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Convolutional spectral kernel learning with generalization guarantees

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.316670Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 68a758b7-c64f-4d77-8f6f-ec29d455510f · outbound

This paper cites Learning in feature spaces via coupled covariances: Asymmetric kernel svd and nyström method.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Learning in feature spaces via coupled covariances: Asymmetric kernel svd and nyström method

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.197321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ea5331a-801f-481d-a747-fb9d2d8c6882 · outbound

This paper cites Nyströmformer: A nyström-based algorithm for approximating self-attention.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Nyströmformer: A nyström-based algorithm for approximating self-attention

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:03.304527Z

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Observation 83ff2993-41c0-4335-9405-b4235ff0d055 · outbound

This paper cites Nyström method for accurate and scalable implicit differentiation.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Nyström method for accurate and scalable implicit differentiation

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:06.027863Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fabfdafc-923e-4cd5-a0ea-b143585f5d84 · outbound

This paper cites Deep kernel learning.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Deep kernel learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:03.535801Z

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Observation bb549026-f827-46a0-b2ca-80ecb913c8ff · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Reading digits in natural images with unsupervised feature learning

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:26:05.917728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7149aa74-f104-4e33-bdbb-bf0e1dfe365c · outbound

This paper cites TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:03.738888Z

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Observation b555016f-7697-465f-a54c-d2b3e922677b · outbound

This paper cites an unresolved cited work.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation Unresolved cited work

Reference 92

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:26:05.717718Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aed67d89-bedd-445f-b692-2916f082fe97 · outbound

This paper cites B.1 Experiments on MobileNet Table 6 supplements detection results on the ImageNet-1K benchmark and MobileNetV2.

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation B.1 Experiments on MobileNet Table 6 supplements detection results on the ImageNet-1K benchmark and MobileNetV2

Reference 93

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T15:26:05.604352Z

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