Paper Citation Record · LEDGER

CAdam: Confidence-Based Optimization for Online Learning

As of 22 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.19647.

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

pith.paper-citation-record.v1
2411.19647 v2

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T06:03:51.481245Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

verified exact3
verified fuzzy18
unresolved27
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f28ca40-84aa-46ee-9cd3-c3d26a89dbca · outbound

This paper cites A survey of recommendation systems: recommendation models, techniques, and application fields.

CAdam: Confidence-Based Optimization for Online Learning A survey of recommendation systems: recommendation models, techniques, and application fields

Reference 1

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Observation 747e78eb-e1b1-47a5-87e2-496798497819 · outbound

This paper cites Kingma and Jimmy Ba.

CAdam: Confidence-Based Optimization for Online Learning Kingma and Jimmy Ba

Reference 2

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Observation 584f5865-9668-425e-89a4-29dd090a9eb2 · outbound

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

CAdam: Confidence-Based Optimization for Online Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 3

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Observation d2614edf-da7f-40aa-a7d2-a315eaf96188 · outbound

This paper cites Attention is all you need.

CAdam: Confidence-Based Optimization for Online Learning Attention is all you need

Reference 4

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Observation b58ef85f-0983-46f5-a574-d22b568606ee · outbound

This paper cites Proximal Policy Optimization Algorithms.

CAdam: Confidence-Based Optimization for Online Learning Proximal Policy Optimization Algorithms

Reference 5

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Observation 21f21328-4846-4e05-94a1-ecb18432d134 · outbound

This paper cites Learning Robust Recommender from Noisy Implicit Feedback.

CAdam: Confidence-Based Optimization for Online Learning Learning Robust Recommender from Noisy Implicit Feedback

Reference 6

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Observation 13051096-e4dd-4a31-bdaf-6e2567287ce3 · outbound

This paper cites A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels.

CAdam: Confidence-Based Optimization for Online Learning A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels

Reference 7

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Observation f4bc754d-48ac-46e4-ba30-7294f5a00109 · outbound

This paper cites On the convergence of adam and beyond.

CAdam: Confidence-Based Optimization for Online Learning On the convergence of adam and beyond

Reference 8

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Observation 93f36c1a-28da-4669-a4c1-daeae047d574 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

CAdam: Confidence-Based Optimization for Online Learning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 9

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Observation c1df9564-64e7-48a5-9c17-c7911d3e6208 · outbound

This paper cites Deep residual learning for image recognition.

CAdam: Confidence-Based Optimization for Online Learning Deep residual learning for image recognition

Reference 10

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Observation 46489e81-5794-4cb0-be97-e3f1722161a5 · outbound

This paper cites Densely connected convolutional networks.

CAdam: Confidence-Based Optimization for Online Learning Densely connected convolutional networks

Reference 11

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Observation 6298fcb6-b52e-448e-a11e-1f278653d7df · outbound

This paper cites Display advertising challenge, 2014.

CAdam: Confidence-Based Optimization for Online Learning Display advertising challenge, 2014

Reference 12

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Observation 2ffe7d01-865b-46dd-b4e2-7a165d137c20 · outbound

This paper cites Open benchmarking for click-through rate prediction.

CAdam: Confidence-Based Optimization for Online Learning Open benchmarking for click-through rate prediction

Reference 13

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Observation 91bb9703-3932-4b08-9fb9-268fa1b40ea0 · outbound

This paper cites Online Robust and Adaptive Learning from Data Streams.

CAdam: Confidence-Based Optimization for Online Learning Online Robust and Adaptive Learning from Data Streams

Reference 14

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Source-reported events for the cited work

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Observation 07e56a4f-8411-4c3d-95e3-b8a1810d2d1b · outbound

This paper cites Adaptive methods for nonconvex optimization.

CAdam: Confidence-Based Optimization for Online Learning Adaptive methods for nonconvex optimization

Reference 15

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Observation 7e4ce5f8-ae17-43a7-a779-efda4c0986a0 · outbound

This paper cites On the Variance of the Adaptive Learning Rate and Beyond.

CAdam: Confidence-Based Optimization for Online Learning On the Variance of the Adaptive Learning Rate and Beyond

Reference 16

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Observation 49e98e4a-baeb-413a-976f-acb49c6382dd · outbound

This paper cites Adabelief optimizer: Adapting stepsizes by the belief in observed gradients.

CAdam: Confidence-Based Optimization for Online Learning Adabelief optimizer: Adapting stepsizes by the belief in observed gradients

Reference 17

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Observation 59d06e60-2770-4eed-9603-62a8d38c82a1 · outbound

This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

CAdam: Confidence-Based Optimization for Online Learning DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 18

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Observation c9e0e72e-6fd0-44ef-9f1c-eee3a307d23f · outbound

This paper cites Wide & deep learning for recommender systems.

CAdam: Confidence-Based Optimization for Online Learning Wide & deep learning for recommender systems

Reference 19

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Observation c8d47ee5-bb60-40ba-8b14-da74863a4e31 · outbound

This paper cites Deep neural networks for youtube recommenda- tions.

CAdam: Confidence-Based Optimization for Online Learning Deep neural networks for youtube recommenda- tions

Reference 20

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Observation 5f000efc-720f-427f-a6ba-9d33a19be70b · outbound

This paper cites Product- based neural networks for user response prediction.

CAdam: Confidence-Based Optimization for Online Learning Product- based neural networks for user response prediction

Reference 21

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Observation 6757b7be-e3b0-4da5-98dc-02609b0dd896 · outbound

This paper cites Deep & cross network for ad click predictions.

CAdam: Confidence-Based Optimization for Online Learning Deep & cross network for ad click predictions

Reference 22

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Observation 84680a68-ba35-493a-ae38-45cee5cf17ea · outbound

This paper cites Symbolic discovery of optimization algorithms.

CAdam: Confidence-Based Optimization for Online Learning Symbolic discovery of optimization algorithms

Reference 23

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Observation fd89be4b-774a-4434-aac6-3c36ad44f258 · outbound

This paper cites Convergence of adam under relaxed assumptions.

CAdam: Confidence-Based Optimization for Online Learning Convergence of adam under relaxed assumptions

Reference 24

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Observation f8e8244e-3c0e-4b6c-a1f0-08073a55b234 · outbound

This paper cites On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond.

CAdam: Confidence-Based Optimization for Online Learning On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond

Reference 25

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Observation d73c2079-354d-4fab-9443-e29a5692c53d · outbound

This paper cites Decoupled Weight Decay Regularization.

CAdam: Confidence-Based Optimization for Online Learning Decoupled Weight Decay Regularization

Reference 26

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Observation fc0854fa-53bf-4faa-afb6-d1520b104ee9 · outbound

This paper cites Adaptive methods for nonconvex optimization.

CAdam: Confidence-Based Optimization for Online Learning Adaptive methods for nonconvex optimization

Reference 27

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Observation b6ca07f7-d128-4cae-9a73-83e8c32cb956 · outbound

This paper cites Cautious optimizers: Improving training with one line of code.

CAdam: Confidence-Based Optimization for Online Learning Cautious optimizers: Improving training with one line of code

Reference 28

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Observation 66029002-2d12-4730-99d5-f31060ddfb41 · outbound

This paper cites Learning under concept drift: A review.

CAdam: Confidence-Based Optimization for Online Learning Learning under concept drift: A review

Reference 29

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Observation 423b2e2b-9ef3-49b1-bc47-1dabf1314280 · outbound

This paper cites Learning from time-changing data with adaptive windowing.

CAdam: Confidence-Based Optimization for Online Learning Learning from time-changing data with adaptive windowing

Reference 30

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Observation 0f06b686-15cd-4c7b-9d0f-aeb8c40bc054 · outbound

This paper cites A streaming ensemble algorithm (sea) for large-scale classification.

CAdam: Confidence-Based Optimization for Online Learning A streaming ensemble algorithm (sea) for large-scale classification

Reference 31

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Observation ea3944be-9b14-4478-a853-56c5411a1796 · outbound

This paper cites Online convex programming and generalized infinitesimal gradient ascent.

CAdam: Confidence-Based Optimization for Online Learning Online convex programming and generalized infinitesimal gradient ascent

Reference 32

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Observation 54ae3778-54be-4d4a-b6c4-256e44dd2679 · outbound

This paper cites Model-agnostic meta-learning for fast adap- tation of deep networks.

CAdam: Confidence-Based Optimization for Online Learning Model-agnostic meta-learning for fast adap- tation of deep networks

Reference 33

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Observation f4f16e56-388f-4c8e-bf00-7b4c9394f83a · outbound

This paper cites A case study of batch and incremental recom- mender systems in supermarket data under concept drifts and cold start.

CAdam: Confidence-Based Optimization for Online Learning A case study of batch and incremental recom- mender systems in supermarket data under concept drifts and cold start

Reference 34

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Observation 43f7dac6-6459-45d3-8197-90fcc08ed126 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

CAdam: Confidence-Based Optimization for Online Learning Robust loss functions under label noise for deep neural networks

Reference 35

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

CAdam: Confidence-Based Optimization for Online Learning Dropout: a simple way to prevent neural networks from overfitting

Reference 36

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This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

CAdam: Confidence-Based Optimization for Online Learning Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 37

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Observation 19dd41f5-dac6-4496-bdbf-80eed2f12714 · outbound

This paper cites Automated Data Denoising for Recommendation.

CAdam: Confidence-Based Optimization for Online Learning Automated Data Denoising for Recommendation

Reference 38

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This paper cites Dtec: Dual training error based correction approach for recommender systems.

CAdam: Confidence-Based Optimization for Online Learning Dtec: Dual training error based correction approach for recommender systems

Reference 39

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Observation d4d5c102-5f65-4cf9-a3de-c839486116ef · outbound

This paper cites Improving recommendation quality through outlier removal.

CAdam: Confidence-Based Optimization for Online Learning Improving recommendation quality through outlier removal

Reference 40

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Observation 63d5073f-4acb-431c-9629-1315f5732ec4 · outbound

This paper cites A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system.

CAdam: Confidence-Based Optimization for Online Learning A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system

Reference 41

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

CAdam: Confidence-Based Optimization for Online Learning Pytorch: An imperative style, high-performance deep learning library

Reference 42

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This paper cites Bars: Towards open benchmarking for recommender systems.

CAdam: Confidence-Based Optimization for Online Learning Bars: Towards open benchmarking for recommender systems

Reference 43

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This paper cites C Additional Experiments C.1 Numerical Experiments Figure 6 illustrate how both optimizers perform in a noise-free environment.

CAdam: Confidence-Based Optimization for Online Learning C Additional Experiments C.1 Numerical Experiments Figure 6 illustrate how both optimizers perform in a noise-free environment

Reference 44

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CAdam: Confidence-Based Optimization for Online Learning Unresolved cited work

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Pith citing papers

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