Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:32:52.846731Z
Paper Citation Record · LEDGER
On Regularization Properties of Artificial Datasets for Deep Learning
As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.07005.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
22 of 22 outbound references displayed
- verified exact0
- verified fuzzy18
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f98301ce-5698-41e4-b5c2-6287da7d0eba · outbound
This paper cites no free lunch.
On Regularization Properties of Artificial Datasets for Deep Learning no free lunch
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.316800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2248ef2e-bf1b-4771-bfc0-15ad8eee8aab · outbound
This paper cites deep learning.
On Regularization Properties of Artificial Datasets for Deep Learning deep learning
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.297470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 361d2783-c496-44c9-940c-2a4619b2199b · outbound
This paper cites Two main factors are involved here.
On Regularization Properties of Artificial Datasets for Deep Learning Two main factors are involved here
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.279876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5d80bc53-b994-4623-ab69-a6f71c923d49 · outbound
This paper cites Let us formalize this randomness by representing it as a vector 𝑟 of random values , called noise vector.
On Regularization Properties of Artificial Datasets for Deep Learning Let us formalize this randomness by representing it as a vector 𝑟 of random values , called noise vector
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.262142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8d46b075-9197-4123-83ea-4c201da5e3b1 · outbound
This paper cites an unresolved cited work.
On Regularization Properties of Artificial Datasets for Deep Learning Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:32:53.236330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f3fa98be-fae6-465c-96f5-9a348224166a · outbound
This paper cites Machine Learning Basics,.
On Regularization Properties of Artificial Datasets for Deep Learning Machine Learning Basics,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.218097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e07cd2c8-b79f-4d4d-a505-3f6170bbe04c · outbound
This paper cites The Lack of A Priori Distinctions Between Learning Algorithms,.
On Regularization Properties of Artificial Datasets for Deep Learning The Lack of A Priori Distinctions Between Learning Algorithms,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.199198Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 155732d5-c09f-4995-b4ef-54470bf74169 · outbound
This paper cites Understanding deep learning requires rethinking generalization.
On Regularization Properties of Artificial Datasets for Deep Learning Understanding deep learning requires rethinking generalization
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:32:52.757994Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd5b442a-733c-48b8-b788-d03aae3492ca · outbound
This paper cites Feature selection, L 1 vs. L 2 regularization, and rotational invariance,.
On Regularization Properties of Artificial Datasets for Deep Learning Feature selection, L 1 vs. L 2 regularization, and rotational invariance,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.180087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6ef2a8e4-180f-4974-bcb1-1a8b9620b77e · outbound
This paper cites Dropout: a simple way to prevent neural networks from overfitting,.
On Regularization Properties of Artificial Datasets for Deep Learning Dropout: a simple way to prevent neural networks from overfitting,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.161975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 399c4228-2dec-414f-840e-042298a16f25 · outbound
This paper cites Regularization of neural networks using dropconnect,.
On Regularization Properties of Artificial Datasets for Deep Learning Regularization of neural networks using dropconnect,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.142261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3675a910-9c89-45ad-9ea5-d9fa1b83b6c0 · outbound
This paper cites Bagging Predictors,.
On Regularization Properties of Artificial Datasets for Deep Learning Bagging Predictors,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.123066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7c947df4-f33a-4347-807f-d009fc424e72 · outbound
This paper cites The Art of Data Augmentation,.
On Regularization Properties of Artificial Datasets for Deep Learning The Art of Data Augmentation,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.106019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 86c17f19-5e86-47a6-a15e-b0ff01b3871b · outbound
This paper cites Convolutional deep belief networks for scalable unsupervised learning of 6 hierarchical representations,.
On Regularization Properties of Artificial Datasets for Deep Learning Convolutional deep belief networks for scalable unsupervised learning of 6 hierarchical representations,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.088114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 14e3a590-f757-470b-8bf5-82980523bb83 · outbound
This paper cites Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,.
On Regularization Properties of Artificial Datasets for Deep Learning Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.066106Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1fe34a9b-0074-4c08-a0ee-c3a1c6923e7b · outbound
This paper cites Unsupervised feature learning for audio classification using convolutional deep belief networks,.
On Regularization Properties of Artificial Datasets for Deep Learning Unsupervised feature learning for audio classification using convolutional deep belief networks,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.044640Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 85360c9d-7e91-440f-b3d7-f8641e2a7de4 · outbound
This paper cites Training with Noise is Equivalent to Tikhonov Regularization,.
On Regularization Properties of Artificial Datasets for Deep Learning Training with Noise is Equivalent to Tikhonov Regularization,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.026988Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 76eee14f-259d-4501-97e9-bce27a71d6a9 · outbound
This paper cites When Does Label Smoothing Help?.
On Regularization Properties of Artificial Datasets for Deep Learning When Does Label Smoothing Help?
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:32:52.820743Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e185a014-d64b-44fd-b891-46df57558159 · outbound
This paper cites Stenosis Detection with Deep Convolutional Neural Networks,.
On Regularization Properties of Artificial Datasets for Deep Learning Stenosis Detection with Deep Convolutional Neural Networks,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:53.007383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8ace979b-806c-4257-baa6-3163d76ed164 · outbound
This paper cites Deep Recurrent Neural Networks for ECG Signal Denoising.
On Regularization Properties of Artificial Datasets for Deep Learning Deep Recurrent Neural Networks for ECG Signal Denoising
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:32:52.834738Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c835f20b-478e-44c5-816f-1eb35363e103 · outbound
This paper cites Rademacher and Gaussian Co mplexities: Risk Bounds and Structural Results,.
On Regularization Properties of Artificial Datasets for Deep Learning Rademacher and Gaussian Co mplexities: Risk Bounds and Structural Results,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:52.986333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 91cfbf74-c4a0-40a2-a7e5-b5478740c090 · outbound
This paper cites On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities,.
On Regularization Properties of Artificial Datasets for Deep Learning On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:32:52.965502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.