Uncertainty Quantification in Continual Open-World Learning | Paper Citation Record

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This paper cites Continual evidential deep learning for out- of-distribution detection.

Uncertainty Quantification in Continual Open-World Learning Continual evidential deep learning for out- of-distribution detection

Reference 1

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Observation 607b5e8d-1bec-4325-9000-c34e8f241a4c · outbound

This paper cites Ahuja, Ibrahima J.

Uncertainty Quantification in Continual Open-World Learning Ahuja, Ibrahima J

Reference 2

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Observation 6c40e523-a18a-440c-ae01-b3277a074ba9 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Uncertainty Quantification in Continual Open-World Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

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Observation 232f911e-d8b1-42cb-9251-25d678972c16 · outbound

This paper cites Continual novelty detection.

Uncertainty Quantification in Continual Open-World Learning Continual novelty detection

Reference 4

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Observation 3eda32ed-21b1-45c6-a1ee-2508a1d40097 · outbound

This paper cites Few-shot continual active learning by a robot.

Uncertainty Quantification in Continual Open-World Learning Few-shot continual active learning by a robot

Reference 5

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Observation ea390997-100a-4443-99e1-db96a52fe1a1 · outbound

This paper cites Few-shot continual active learning by a robot.

Uncertainty Quantification in Continual Open-World Learning Few-shot continual active learning by a robot

Reference 6

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Observation be942391-09ef-4132-ba58-e65517d3a20c · outbound

This paper cites Beyond Supervised Continual Learning: a Review.

Uncertainty Quantification in Continual Open-World Learning Beyond Supervised Continual Learning: a Review

Reference 7

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This paper cites Mixmatch: A holistic approach to semi-supervised learning.

Uncertainty Quantification in Continual Open-World Learning Mixmatch: A holistic approach to semi-supervised learning

Reference 8

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Observation 11497a44-f370-4b2e-b2a3-7a26d6a86d73 · outbound

This paper cites Continual semi-supervised learning through contrastive interpolation consistency.

Uncertainty Quantification in Continual Open-World Learning Continual semi-supervised learning through contrastive interpolation consistency

Reference 9

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Observation 05e6c474-920b-4dd5-b4b5-16088c9fc40d · outbound

This paper cites Continual semi-supervised learning through contrastive interpolation consistency.

Uncertainty Quantification in Continual Open-World Learning Continual semi-supervised learning through contrastive interpolation consistency

Reference 10

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Observation a7f09976-0548-42a4-b4e9-f55ec1bdb4d8 · outbound

This paper cites Incorporating diversity in active learning with support vector machines.

Uncertainty Quantification in Continual Open-World Learning Incorporating diversity in active learning with support vector machines

Reference 11

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Observation 7aa716e9-409a-4cc0-ae86-2e7c767741fe · outbound

This paper cites Rethinking experience replay: a bag of tricks for continual learning.

Uncertainty Quantification in Continual Open-World Learning Rethinking experience replay: a bag of tricks for continual learning

Reference 12

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Observation 2e6ef21b-fcf8-4c7f-b667-c5c09137793e · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Uncertainty Quantification in Continual Open-World Learning Unsupervised learning of visual features by contrasting cluster assignments

Reference 13

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Observation 8f27530b-3f67-4e9f-8cdc-8757bbf55a24 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Uncertainty Quantification in Continual Open-World Learning Emerg- ing properties in self-supervised vision transformers

Reference 14

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This paper cites Superposition of many mod- els into one.

Uncertainty Quantification in Continual Open-World Learning Superposition of many mod- els into one

Reference 15

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Observation ebbc9bdd-e9ea-4d47-967e-ce99cd6b400b · outbound

This paper cites Breaking the closed world assump- tion in text classification.

Uncertainty Quantification in Continual Open-World Learning Breaking the closed world assump- tion in text classification

Reference 16

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Observation cb95c65f-72a3-4fba-9e89-5fafeee2c664 · outbound

This paper cites Catastrophic forgetting in connectionist networks.

Uncertainty Quantification in Continual Open-World Learning Catastrophic forgetting in connectionist networks

Reference 17

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Observation b3579f1b-4f96-4a53-a2a9-9520e618080b · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Uncertainty Quantification in Continual Open-World Learning Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 18

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Observation 067749bf-cf5c-4430-8dd9-10493c9bff45 · outbound

This paper cites Deep bayesian active learning with image data.

Uncertainty Quantification in Continual Open-World Learning Deep bayesian active learning with image data

Reference 19

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Observation af7c5339-8494-4144-b236-5aa8c770444c · outbound

This paper cites Learning to discover novel visual categories via deep transfer cluster- ing.

Uncertainty Quantification in Continual Open-World Learning Learning to discover novel visual categories via deep transfer cluster- ing

Reference 20

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Observation 2bef2bd5-24d9-46e2-b244-ed1287970113 · outbound

This paper cites Deep residual learning for image recognition.

Uncertainty Quantification in Continual Open-World Learning Deep residual learning for image recognition

Reference 21

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Observation d49091be-bb15-4622-b5e6-efd3b2b25463 · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019.

Uncertainty Quantification in Continual Open-World Learning Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2019

Reference 22

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Observation 6fb8067d-7fcf-4073-b327-bd1d90bc22a5 · outbound

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

Uncertainty Quantification in Continual Open-World Learning A baseline for detect- ing misclassified and out-of-distribution examples in neural networks

Reference 23

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Observation 1147a8cb-7a18-4916-9492-1c1d32d0333a · outbound

This paper cites The inaturalist species classification and de- tection dataset, 2018.

Uncertainty Quantification in Continual Open-World Learning The inaturalist species classification and de- tection dataset, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.958920Z

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Observation e4538f1a-60f9-417b-9d33-c750b5b9dcf3 · outbound

This paper cites A soft nearest-neighbor framework for con- tinual semi-supervised learning.

Uncertainty Quantification in Continual Open-World Learning A soft nearest-neighbor framework for con- tinual semi-supervised learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.941422Z

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Observation 7f08388c-e1c0-439b-b5f3-9dea1b58617d · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Uncertainty Quantification in Continual Open-World Learning Overcoming catastrophic forgetting in neu- ral networks

Reference 26

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Observation 727b887e-afd9-4d0e-8fbe-54d60ab478df · outbound

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

Uncertainty Quantification in Continual Open-World Learning Learning multiple layers of features from tiny images

Reference 27

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Observation a89dacae-7763-4a0a-8de0-f8be44ed10fe · outbound

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

Uncertainty Quantification in Continual Open-World Learning A simple unified framework for detecting out-of-distribution samples and adversarial attacks

Reference 28

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Observation bda8fd2e-09cf-4dec-a459-8bda32819d4d · outbound

This paper cites Lewis and William A.

Uncertainty Quantification in Continual Open-World Learning Lewis and William A

Reference 29

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Observation 986e406d-a970-4554-ab45-3507c53f671c · outbound

This paper cites Enhancing the re- liability of out-of-distribution image detection in neural net- works.

Uncertainty Quantification in Continual Open-World Learning Enhancing the re- liability of out-of-distribution image detection in neural net- works

Reference 30

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Observation a1e4674c-9831-433f-b4ce-7b991dc68fed · outbound

This paper cites Active generalized category discovery.

Uncertainty Quantification in Continual Open-World Learning Active generalized category discovery

Reference 31

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Observation bfc4e0bb-45bd-40d1-9b7d-c398ef4fc279 · outbound

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

Uncertainty Quantification in Continual Open-World Learning Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features

Reference 32

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Observation bbbb46a6-ba7d-41d4-82b8-b31d1bce4e8e · outbound

This paper cites Sub- space modeling for fast out-of-distribution and anomaly de- tection.

Uncertainty Quantification in Continual Open-World Learning Sub- space modeling for fast out-of-distribution and anomaly de- tection

Reference 33

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Observation 4f871aa9-a570-49d2-9415-be8b5e868f6f · outbound

This paper cites Continual lifelong learning with neural networks: A review.

Uncertainty Quantification in Continual Open-World Learning Continual lifelong learning with neural networks: A review

Reference 34

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Observation 60f2d810-52e8-4416-83bb-231dbcd54356 · outbound

This paper cites Open-world machine learning: appli- cations, challenges, and opportunities.

Uncertainty Quantification in Continual Open-World Learning Open-world machine learning: appli- cations, challenges, and opportunities

Reference 35

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Observation 22466378-0363-415f-b1e4-29603142a891 · outbound

This paper cites icarl: Incremental classifier and representation learning.

Uncertainty Quantification in Continual Open-World Learning icarl: Incremental classifier and representation learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.770849Z

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Observation f03eece0-78c9-4ae3-ba07-ec53349a2bcc · outbound

This paper cites Likelihood ratios for out-of-distribution detec- tion.

Uncertainty Quantification in Continual Open-World Learning Likelihood ratios for out-of-distribution detec- tion

Reference 37

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Observation 7f899090-7e70-46f4-8282-145761399a30 · outbound

This paper cites Gupta, Xiaojiang Chen, and Xin Wang.

Uncertainty Quantification in Continual Open-World Learning Gupta, Xiaojiang Chen, and Xin Wang

Reference 38

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Observation 7cdd0180-ce2e-4e46-84e4-5b70b81321f7 · outbound

This paper cites Imagenet-21k pretraining for the masses,.

Uncertainty Quantification in Continual Open-World Learning Imagenet-21k pretraining for the masses,

Reference 39

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Observation 2558926c-6830-4820-aad7-98ef89da1083 · outbound

This paper cites Closed-Loop Memory GAN for Continual Learning.

Uncertainty Quantification in Continual Open-World Learning Closed-Loop Memory GAN for Continual Learning

Reference 40

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Observation 583c0e2c-1456-4f73-90cc-885e5ccb7feb · outbound

This paper cites Lifelong learning without a task oracle.

Uncertainty Quantification in Continual Open-World Learning Lifelong learning without a task oracle

Reference 41

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Observation 833e96f8-0706-4b5d-bd81-9be0dacf8323 · outbound

This paper cites incdfm: Incremental deep feature modeling for continual novelty detection.

Uncertainty Quantification in Continual Open-World Learning incdfm: Incremental deep feature modeling for continual novelty detection

Reference 42

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Observation 7a7402a4-b0f0-4114-b1d5-bf8965f3de00 · outbound

This paper cites Experience replay for continual learning.

Uncertainty Quantification in Continual Open-World Learning Experience replay for continual learning

Reference 43

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Observation 81eb4155-b9f8-409e-938b-e2e9499e7485 · outbound

This paper cites Active learning for convolu- tional neural networks: A core-set approach.

Uncertainty Quantification in Continual Open-World Learning Active learning for convolu- tional neural networks: A core-set approach

Reference 44

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Observation c815abaa-5903-40f5-b0c9-1d00175cacdc · outbound

This paper cites Active learning literature survey.

Uncertainty Quantification in Continual Open-World Learning Active learning literature survey

Reference 45

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Observation 3f308fa2-251a-4245-9c9d-30094e8f67bc · outbound

This paper cites Doc: Deep open classifica- tion of text documents.

Uncertainty Quantification in Continual Open-World Learning Doc: Deep open classifica- tion of text documents

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.619393Z

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Observation 6bdac989-6eef-4d23-9adb-dbf102f69f18 · outbound

This paper cites Support vector machine ac- tive learning with applications to text classification.J.

Uncertainty Quantification in Continual Open-World Learning Support vector machine ac- tive learning with applications to text classification.J

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.599432Z

Source-reported events for the cited work

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Observation 6e69898c-fee9-4e25-9fd5-7143d5dc0d50 · outbound

This paper cites Generalized category discovery.

Uncertainty Quantification in Continual Open-World Learning Generalized category discovery

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.578505Z

Source-reported events for the cited work

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Observation 4b660dff-49d8-42d4-86c6-c4c0035df043 · outbound

This paper cites Active Continual Learning: On Balancing Knowledge Retention and Learnability.

Uncertainty Quantification in Continual Open-World Learning Active Continual Learning: On Balancing Knowledge Retention and Learnability

Reference 49

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Source-reported events for the cited work

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Observation a5d06529-2b40-4471-95a4-421811135776 · outbound

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

Uncertainty Quantification in Continual Open-World Learning Vim: Out-of-distribution with virtual-logit matching

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.561117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e8467e9-d71e-4f81-8b09-8a07820c5e6c · outbound

This paper cites Beneficial perturbation network for designing general adap- tive artificial intelligence systems.

Uncertainty Quantification in Continual Open-World Learning Beneficial perturbation network for designing general adap- tive artificial intelligence systems

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.540291Z

Source-reported events for the cited work

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Observation 82c11345-bdea-4859-adf2-6836e6ab2bd4 · outbound

This paper cites Learning loss for active learning.

Uncertainty Quantification in Continual Open-World Learning Learning loss for active learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.515182Z

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Observation ef79393e-3f4d-49da-98ef-29599c95484b · outbound

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

Uncertainty Quantification in Continual Open-World Learning Places: A 10 million image database for scene recognition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:40:37.492699Z

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Pith citing papers