Paper Citation Record · LEDGER

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks

As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:1908.06395.

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

pith.paper-citation-record.v1
1908.06395 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:56:20.157510Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

36 of 36 outbound references displayed

verified exact1
verified fuzzy16
unresolved19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eeccf434-d01f-484c-abab-020c0610a1f3 · outbound

This paper cites Katyusha: The ﬁrst direct acceleration of stochastic gradient methods.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Katyusha: The ﬁrst direct acceleration of stochastic gradient methods

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.401443Z

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Observation 15c4b2f3-b5c9-4442-b8cf-205045506f88 · outbound

This paper cites Improved svrg for non-strongly-convex or sum-of-non- convex objectives.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Improved svrg for non-strongly-convex or sum-of-non- convex objectives

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.393678Z

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Observation 9fdd86f7-dcdb-41c5-8538-3a6b4fe2c4d6 · outbound

This paper cites On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Reference 3

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Observation 13b11110-a481-45dd-bc92-2511c1a5f21c · outbound

This paper cites Understanding batch normalization.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Understanding batch normalization

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.385621Z

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Observation 2585a2bf-102b-4f8c-b33c-a6c9bec45dfb · outbound

This paper cites On the Ineffectiveness of Variance Reduced Optimization for Deep Learning.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

Reference 5

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Observation b37f3ae8-a9d1-47cc-b75f-8109aa97feb0 · outbound

This paper cites Saga: A fast incremental gradient method with support for non-strongly convex composite objectives.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Saga: A fast incremental gradient method with support for non-strongly convex composite objectives

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.378208Z

Source-reported events for the cited work

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Observation ecf2ef70-bbe2-4e88-af49-51442f4fa567 · outbound

This paper cites Accelerated gradient methods for nonconvex nonlinear and stochastic programming.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Accelerated gradient methods for nonconvex nonlinear and stochastic programming

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.370510Z

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Observation c8493890-4abe-4bdf-8245-dedb836a05f9 · outbound

This paper cites Deep learning.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Deep learning

Reference 8

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Observation def54e6c-5163-4fee-b2ea-d92afe92f331 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 9

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Source-reported events for the cited work

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Observation e78087da-772f-456b-aca3-d4ff600f26c8 · outbound

This paper cites Stopwasting my gradients: Practical svrg.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Stopwasting my gradients: Practical svrg

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.358757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7b3a1981-09b1-455d-89fe-8d6fba077940 · outbound

This paper cites Deep residual learning for image recognition.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Deep residual learning for image recognition

Reference 11

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Source-reported events for the cited work

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Observation bade68b0-de08-4900-a859-719d44bc812d · outbound

This paper cites Train longer, generalize better: closing the generalization gap in large batch training of neural networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Train longer, generalize better: closing the generalization gap in large batch training of neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.347293Z

Source-reported events for the cited work

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Observation 6ab70562-79bb-44de-b2aa-b0579ba5f4a8 · outbound

This paper cites Deep networks with stochastic depth.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Deep networks with stochastic depth

Reference 13

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Source-reported events for the cited work

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Observation 01aaad5a-f115-4b3b-9421-1b160d2f3e76 · outbound

This paper cites Densely connected convolutional networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Densely connected convolutional networks

Reference 14

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Source-reported events for the cited work

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Observation e95e0c57-b4e7-4675-9e29-f01ede05300d · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 15

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Observation 51eaa0bf-c206-4497-81a3-9b5499b03904 · outbound

This paper cites Linear convergence of gradient and proximal- gradient methods under the polyak-łojasiewicz condition.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Linear convergence of gradient and proximal- gradient methods under the polyak-łojasiewicz condition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.331748Z

Source-reported events for the cited work

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Observation 03243838-ca4d-4a37-9d34-107ee5e13cf9 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 17

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Source-reported events for the cited work

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Observation 5ff16e12-13f9-4117-948c-df290ee6a518 · outbound

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

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Learning multiple layers of features from tiny images

Reference 18

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Source-reported events for the cited work

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Observation 4b5f94ef-67ad-4501-909f-10a115668682 · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Imagenet classiﬁcation with deep convolutional neural networks

Reference 19

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Source-reported events for the cited work

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Observation 3fa7504e-9218-4a3e-99c2-7caa7af0b15d · outbound

This paper cites Non-convex ﬁnite-sum optimization via scsg methods.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Non-convex ﬁnite-sum optimization via scsg methods

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.316254Z

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Observation 1061e6fa-b21c-421e-95d3-09e3f30115c2 · outbound

This paper cites Introductory lectures on convex optimization: A basic course , volume 87.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Introductory lectures on convex optimization: A basic course , volume 87

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.308211Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c36acfa2-8cd4-4cab-82d7-b2fb7bed2c25 · outbound

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

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Reading digits in natural images with unsupervised feature learning

Reference 22

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Observation b7d8f731-2b0e-4979-90db-fa023abe1da8 · outbound

This paper cites Data-Dependent Path Normalization in Neural Networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Data-Dependent Path Normalization in Neural Networks

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:56:20.200955Z

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Observation 36b536ce-cc14-46ec-8baf-fb7aabe9d28f · outbound

This paper cites Norm-based capacity control in neural networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Norm-based capacity control in neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.296647Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c9aee79-ee81-45c6-a603-a9d90fd6ae24 · outbound

This paper cites Exploring generalization in deep learning.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Exploring generalization in deep learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.288870Z

Source-reported events for the cited work

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Observation 841df54e-9660-4319-ad43-6ab7b8f07696 · outbound

This paper cites Automatic differentiation in pytorch.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Automatic differentiation in pytorch

Reference 26

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Observation 81685990-3cbf-4616-bcb8-138d74fd826c · outbound

This paper cites Gradient methods for minimizing functionals.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Gradient methods for minimizing functionals

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.277168Z

Source-reported events for the cited work

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Observation c3377c9d-1374-4ac7-a2bb-ac0c484a2cfa · outbound

This paper cites Stochastic variance reduction for nonconvex optimization.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Stochastic variance reduction for nonconvex optimization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.269694Z

Source-reported events for the cited work

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Observation d1f05e16-8cb5-4973-b9dd-5af7f246759a · outbound

This paper cites Minimizing ﬁnite sums with the stochastic average gradient.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Minimizing ﬁnite sums with the stochastic average gradient

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:56:20.262164Z

Source-reported events for the cited work

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Observation e848cea4-85dd-4380-b594-20ed873e28ed · outbound

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

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

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Observation cf504f9f-5d0a-4baa-86e5-f7ac7ad2276d · outbound

This paper cites A Bayesian Perspective on Generalization and Stochastic Gradient Descent.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks A Bayesian Perspective on Generalization and Stochastic Gradient Descent

Reference 31

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Observation fb353f36-9dac-425c-b01b-111008bf0ee7 · outbound

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

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Dropout: a simple way to prevent neural networks from overﬁtting

Reference 32

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Source-reported events for the cited work

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Observation e2dd446f-560b-4dad-9798-8e23a8631b03 · outbound

This paper cites Training very deep networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Training very deep networks

Reference 33

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Observation b38998bc-1f4c-4c4b-b696-27b0619a786b · outbound

This paper cites On the importance of initialization and momentum in deep learning.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks On the importance of initialization and momentum in deep learning

Reference 34

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Observation 934a11fe-922c-4114-8fc8-efe5ee471ce7 · outbound

This paper cites Wide Residual Networks.

Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Wide Residual Networks

Reference 35

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Observation 445e25ae-3f2c-45d6-a486-41a8833e9e1b · outbound

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Towards Better Generalization: BP-SVRG in Training Deep Neural Networks Unresolved cited work

Reference 64

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Pith citing papers

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