Paper Citation Record · LEDGER

Submodular Batch Selection for Training Deep Neural Networks

As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:1906.08771.

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

pith.paper-citation-record.v1
1906.08771 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T19:30:16.773397Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:10:00.374069Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:10:19.806978Z

Reference resolution

34 of 34 outbound references displayed

verified exact10
verified fuzzy24
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7ac708fb-a5ef-40cc-b3bd-7f9b2628094e · outbound

This paper cites Variance Reduction in SGD by Distributed Importance Sampling.

Submodular Batch Selection for Training Deep Neural Networks Variance Reduction in SGD by Distributed Importance Sampling

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.045046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 69d1c75b-62c6-43b2-8ab6-901660b7e5d2 · outbound

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

Submodular Batch Selection for Training Deep Neural Networks Katyusha: The first direct acceleration of stochastic gradient methods

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.065974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32bb27c8-f1b8-4cfd-ac73-8275dc2be008 · outbound

This paper cites Subset replay based continual learning for scalable improvement of autonomous systems.

Submodular Batch Selection for Training Deep Neural Networks Subset replay based continual learning for scalable improvement of autonomous systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.069756Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 34dda67f-d8c9-4e96-8e05-8be861ab6b9d · outbound

This paper cites Adaptive batch mode active learning.

Submodular Batch Selection for Training Deep Neural Networks Adaptive batch mode active learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.054038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 441617fb-460f-465a-a19a-c733c985b9b3 · outbound

This paper cites Active bias: Training more accurate neural networks by emphasizing high variance samples.

Submodular Batch Selection for Training Deep Neural Networks Active bias: Training more accurate neural networks by emphasizing high variance samples

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.050615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3c55f8fc-99e9-4e7f-b9dc-3e6a4d3ba511 · outbound

This paper cites Algorithms for subset selection in linear regression.

Submodular Batch Selection for Training Deep Neural Networks Algorithms for subset selection in linear regression

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.062334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3c356995-33a9-4c2f-961c-0cea9bd18cd4 · outbound

This paper cites Deep residual learning for image recognition.

Submodular Batch Selection for Training Deep Neural Networks Deep residual learning for image recognition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.073051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1d4e560c-f492-4429-b163-89804388c4f6 · outbound

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

Submodular Batch Selection for Training Deep Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.050218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7523e112-2be1-4768-97c4-a130defa437d · outbound

This paper cites Accelerating stochastic gradient descent using predictive variance reduction.

Submodular Batch Selection for Training Deep Neural Networks Accelerating stochastic gradient descent using predictive variance reduction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.058642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b52246e-dd1f-4491-a09f-e09c69a20d0e · outbound

This paper cites Biased Importance Sampling for Deep Neural Network Training.

Submodular Batch Selection for Training Deep Neural Networks Biased Importance Sampling for Deep Neural Network Training

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:09.978383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec27d815-2d30-4d10-a474-5666d7e3ff17 · outbound

This paper cites Not All Samples Are Created Equal: Deep Learning with Importance Sampling.

Submodular Batch Selection for Training Deep Neural Networks Not All Samples Are Created Equal: Deep Learning with Importance Sampling

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-25T19:31:10.033675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a552e09-40d1-470e-a902-335d3a2c82fe · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Submodular Batch Selection for Training Deep Neural Networks Adam: A Method for Stochastic Optimization

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:09.991266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 292eff2f-ecc0-48a8-8171-c87a514d05c0 · outbound

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

Submodular Batch Selection for Training Deep Neural Networks Learning multiple layers of features from tiny images

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.087885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 830fab51-db56-4ab4-ab85-2db0ef423981 · outbound

This paper cites Efficient mini-batch training for stochastic optimization.

Submodular Batch Selection for Training Deep Neural Networks Efficient mini-batch training for stochastic optimization

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.091973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a1db0a72-f710-47fb-8a0e-484fc3279843 · outbound

This paper cites Fast DPP Sampling for Nystr\"om with Application to Kernel Methods.

Submodular Batch Selection for Training Deep Neural Networks Fast DPP Sampling for Nystr\"om with Application to Kernel Methods

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.027375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation eab09472-953a-4450-8ac6-da5b3b68649a · outbound

This paper cites A class of submodular functions for document summarization.

Submodular Batch Selection for Training Deep Neural Networks A class of submodular functions for document summarization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.125267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3b7b615f-31bf-4391-bfe5-dc27a7d5494f · outbound

This paper cites Online batch selection for faster training of neural networks.

Submodular Batch Selection for Training Deep Neural Networks Online batch selection for faster training of neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.136336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 14e57870-b803-4a5a-b737-0bbd80a795b7 · outbound

This paper cites Accelerated greedy algorithms for maximizing submodular set functions.

Submodular Batch Selection for Training Deep Neural Networks Accelerated greedy algorithms for maximizing submodular set functions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.118059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25141e48-d82f-41be-abbf-2975213efa40 · outbound

This paper cites Distributed submodular maximization: Identifying representative elements in massive data.

Submodular Batch Selection for Training Deep Neural Networks Distributed submodular maximization: Identifying representative elements in massive data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.111133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 981e6ce9-2db8-45c2-a1a8-50c034b73977 · outbound

This paper cites Lazier than lazy greedy.

Submodular Batch Selection for Training Deep Neural Networks Lazier than lazy greedy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.128881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1223e7b5-7c31-44ee-a608-8879f8a2dfdd · outbound

This paper cites An analysis of approximations for maximizing submodular set functions—i.

Submodular Batch Selection for Training Deep Neural Networks An analysis of approximations for maximizing submodular set functions—i

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.076715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 626399d9-fc02-4802-aef4-906a56ff623a · outbound

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

Submodular Batch Selection for Training Deep Neural Networks Reading digits in natural images with unsupervised feature learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.080553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1e03549f-3b5c-44d3-92ed-e90081900c6e · outbound

This paper cites Automatic differentiation in pytorch.

Submodular Batch Selection for Training Deep Neural Networks Automatic differentiation in pytorch

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.083833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 17dabc13-e752-4973-8223-c4c253fbb2b6 · outbound

This paper cites Greedy sensor selection: Leveraging submodularity.

Submodular Batch Selection for Training Deep Neural Networks Greedy sensor selection: Leveraging submodularity

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.095438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1f357ab8-440c-4a4e-b513-58ab84f5f208 · outbound

This paper cites Submodular importance sampling for neural network training.

Submodular Batch Selection for Training Deep Neural Networks Submodular importance sampling for neural network training

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.121611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd3c28b2-10c4-4511-9ac9-60155602d147 · outbound

This paper cites Self-Paced Learning with Adaptive Deep Visual Embeddings.

Submodular Batch Selection for Training Deep Neural Networks Self-Paced Learning with Adaptive Deep Visual Embeddings

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.014243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8f049246-dfdb-4142-9614-ae217d49234a · outbound

This paper cites Submodular subset selection for large-scale speech training data.

Submodular Batch Selection for Training Deep Neural Networks Submodular subset selection for large-scale speech training data

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.132691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 706626f3-0eb0-45df-a9f6-e3a68d062b9e · outbound

This paper cites Submodularity in data subset selection and active learning.

Submodular Batch Selection for Training Deep Neural Networks Submodularity in data subset selection and active learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.114589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53650e8f-12c3-4c40-86a4-0d67ee070914 · outbound

This paper cites Determinantal Point Processes for Mini-Batch Diversification.

Submodular Batch Selection for Training Deep Neural Networks Determinantal Point Processes for Mini-Batch Diversification

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:09.984728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6677470d-16be-4b29-b60e-5d4ebb56a042 · outbound

This paper cites Active Mini-Batch Sampling using Repulsive Point Processes.

Submodular Batch Selection for Training Deep Neural Networks Active Mini-Batch Sampling using Repulsive Point Processes

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.021798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 478db85d-19a1-4ca4-858c-3352a7f39be7 · outbound

This paper cites Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling.

Submodular Batch Selection for Training Deep Neural Networks Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-05-25T19:31:10.039386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b91b182b-38cf-4113-b42c-0cf77cdd8142 · outbound

This paper cites Stochastic optimization with importance sampling for regularized loss minimization.

Submodular Batch Selection for Training Deep Neural Networks Stochastic optimization with importance sampling for regularized loss minimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.099322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 90443316-df09-41ea-98b0-41417c90bbf9 · outbound

This paper cites Minimax curriculum learning: Machine teaching with desirable difficulties and scheduled diversity.

Submodular Batch Selection for Training Deep Neural Networks Minimax curriculum learning: Machine teaching with desirable difficulties and scheduled diversity

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.107370Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6e6a5e7e-2323-4a8a-9424-18c0db30b3cd · outbound

This paper cites write newline.

Submodular Batch Selection for Training Deep Neural Networks write newline

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T19:31:11.103446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 6183b426-8b6c-4f08-b30e-8c06e137dc30 · inbound

Learning from Limited and Imperfect Data cites this paper.

Learning from Limited and Imperfect Data Submodular Batch Selection for Training Deep Neural Networks

Reference 128

Resolution: verified exact; local_arxiv, observed 2026-08-06T13:10:19.810916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0b9b2de5-deb5-4dd2-b7aa-276814153fa4 · inbound

A Fast and Effective Method for Euclidean Anticlustering: The Assignment-Based-Anticlustering Algorithm cites this paper.

A Fast and Effective Method for Euclidean Anticlustering: The Assignment-Based-Anticlustering Algorithm Submodular Batch Selection for Training Deep Neural Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T11:31:18.596719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.