Pith. sign in

Paper Citation Record · LEDGER

Stochastic AUC Maximization with Deep Neural Networks

As of 16 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:1908.10831.

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

pith.paper-citation-record.v1
1908.10831 v5

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:41:34.848326Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:38:21.793863Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T05:38:21.907456Z

Reference resolution

54 of 54 outbound references displayed

  • verified exact2
  • verified fuzzy23
  • unresolved28
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 50857d24-7b26-4430-86da-0776e6d82c4a · outbound

This paper cites A Convergence Theory for Deep Learning via Over-Parameterization.

Stochastic AUC Maximization with Deep Neural Networks A Convergence Theory for Deep Learning via Over-Parameterization

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.627188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.627188Z digest=sha256:7addb949674d0687c25fcff48ea224e13cc160a0a1e3ba6c5fbe69a1fd35bee5

Observation 10edc41d-aa1d-4017-bdf9-f6527228fc45 · outbound

This paper cites A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.632775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.632775Z digest=sha256:012fbb3b29d85bcca4287a2b967a6d8f40d58b989a693ee079210bfa3a84befa

Observation 330131e8-4198-4896-ae70-be3e3cc850ca · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Stochastic AUC Maximization with Deep Neural Networks Neural Machine Translation by Jointly Learning to Align and Translate

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.638332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.638332Z digest=sha256:6d17c63121525ac4649fb03cf1816ffb4cf97f27d6a360381f6b609dcab1bcc9

Observation b7703291-0ff7-4312-8418-9732d0ba57ec · outbound

This paper cites Stability and Generalization of Learning Algorithms that Converge to Global Optima.

Stochastic AUC Maximization with Deep Neural Networks Stability and Generalization of Learning Algorithms that Converge to Global Optima

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:41:35.228489Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.643367Z digest=sha256:a9c526c76475ea9badc50952c40273528f0a338614a4dd58f2ed2280b8d24e6d

Observation 0d4d65b9-7c50-4784-9e70-5cc71776280b · outbound

This paper cites Universal stagewise learning for non-convex problems with convergence on averaged solutions.

Stochastic AUC Maximization with Deep Neural Networks Universal stagewise learning for non-convex problems with convergence on averaged solutions

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.628708Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.648617Z digest=sha256:d4b89f58aa1bae5c4d00b103e9f35f8adb0f89345e3e5bebe9f3a07528e8d378

Observation b15282fc-bbf3-4d53-9f8f-a1b3ed4204fd · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Stochastic AUC Maximization with Deep Neural Networks BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.652638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.652638Z digest=sha256:cc4ffefa080e37b78faf5f2a557679afe25b9e0842cc0c59a7ac5ff868086095

Observation ef0e4598-fc11-428f-8e18-5b248ac8905a · outbound

This paper cites Gradient Descent Finds Global Minima of Deep Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks Gradient Descent Finds Global Minima of Deep Neural Networks

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.657220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.657220Z digest=sha256:62b2f476364a0f9f257ed194d43298b2e7a727e78bdf683275b7cd0d9aca2bbf

Observation e945dcf6-3ea2-4384-93c0-10b3fd9334e3 · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.661193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.661193Z digest=sha256:bd27bfcc61421a6a2f47fc47eb70920e1f27a0f11c9571e2d628f4fbfa3c6f3c

Observation 3f787a4c-7fef-49e4-aa60-644ba232a0cc · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Stochastic AUC Maximization with Deep Neural Networks Adaptive subgradient methods for online learning and stochastic optimization

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.664915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.664915Z digest=sha256:a94773ebf08f3e0129c9eafbd827a3dbbf8158739b1f53724a85adbeeef49ad8

Observation 01dd087c-13e7-468c-9485-b0aad00aa297 · outbound

This paper cites Composite objective mirror descent.

Stochastic AUC Maximization with Deep Neural Networks Composite objective mirror descent

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.607388Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.668704Z digest=sha256:078d89ea6208d0bcab3a6ebf1a09bf850bbc780ce3219b8e8033c069d8d3bc25

Observation 78d00cd5-c19e-4133-b0b8-83e3490c5fa6 · outbound

This paper cites The foundations of cost-sensitive learning.

Stochastic AUC Maximization with Deep Neural Networks The foundations of cost-sensitive learning

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.595129Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.672740Z digest=sha256:5bb434c50ee83aaf49c5a90b1c0723e9548680fd698b3950f5119257086e899c

Observation 23897d99-5920-4f3b-9332-9b9852272866 · outbound

This paper cites One-pass auc optimization.

Stochastic AUC Maximization with Deep Neural Networks One-pass auc optimization

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.584495Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.676456Z digest=sha256:d4f866b0c52f2a8586697de58a095b9202ba63f28e3f18a95200b6fdcc3447ff

Observation 83331f5b-03fd-455b-aa6d-66138a73cfa8 · outbound

This paper cites Generating Sequences With Recurrent Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks Generating Sequences With Recurrent Neural Networks

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.680037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.680037Z digest=sha256:bca37f5339b4e05d3ac500692356b097cdd8384bf193a9330aa3c38f2e31403c

Observation e145f71b-eb4e-4592-b225-f6aa15d18b19 · outbound

This paper cites A simple generalisation of the area under the roc curve for multiple class classification problems.

Stochastic AUC Maximization with Deep Neural Networks A simple generalisation of the area under the roc curve for multiple class classification problems

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.573153Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.684722Z digest=sha256:ac2f295bacb7537a088e7622353b15202b77457ad8d22b76d35ddc7cb52b005e

Observation a6e849fa-ec85-40f3-a111-e8c9cb1d3efd · outbound

This paper cites The meaning and use of the area under a receiver operating characteristic (roc) curve.

Stochastic AUC Maximization with Deep Neural Networks The meaning and use of the area under a receiver operating characteristic (roc) curve

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.559947Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.688605Z digest=sha256:7952135791141fa5ac5f860f56d02ecb3d34654ead8fb241da494e3b0c234b5f

Observation f0304677-fe6c-4e82-912d-7b0f06491e87 · outbound

This paper cites A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

Stochastic AUC Maximization with Deep Neural Networks A method of comparing the areas under receiver operating characteristic curves derived from the same cases

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.546915Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.693112Z digest=sha256:9f820b96465227631754f7a782e2771bb90a54aa75d7fd8cda87018421ee3564

Observation 3dbd4523-9b1e-43f6-8ff9-664465b498f1 · outbound

This paper cites Identity Matters in Deep Learning.

Stochastic AUC Maximization with Deep Neural Networks Identity Matters in Deep Learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.697486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.697486Z digest=sha256:1d1a8ec06e076458f5072c2391c01551368fa55834c06647a8fea36262b3cd1d

Observation 813c97ca-2ef5-45d1-8d4a-25dacbc945da · outbound

This paper cites Deep residual learning for image recognition.

Stochastic AUC Maximization with Deep Neural Networks Deep residual learning for image recognition

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.701594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.701594Z digest=sha256:e5df5373729c41925efedbd6b06284b4bb34b5f505694047a8a70fe159b081ea

Observation 48adb466-0b79-4bb2-a20a-fcf9a573efe7 · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition.

Stochastic AUC Maximization with Deep Neural Networks Deep neural networks for acoustic modeling in speech recognition

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.705854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.705854Z digest=sha256:e1c108a673e843a4764df72c4aa176048dd783458fbf2c26f0c3d5cb53e8d43e

Observation b4e21477-1f14-4947-a95c-d1976b49943d · outbound

This paper cites What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?.

Stochastic AUC Maximization with Deep Neural Networks What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.709578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.709578Z digest=sha256:b31c36f10265c4e306fa7b39339cd755d6dfabd95f3aa1e3071474e5223f6c62

Observation 9343a66b-bfa4-40e7-b289-f93855e378e1 · outbound

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

Stochastic AUC Maximization with Deep Neural Networks Linear convergence of gradient and proximal-gradient methods under the polyak- ojasiewicz condition

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.714315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.714315Z digest=sha256:5d0b23dd8a9856d3a0a7a94611cca7c70840b8fd0d753e5009d6a2939d21b30b

Observation 371264b0-cc00-472d-91a1-31b597c119c4 · outbound

This paper cites An Alternative View: When Does SGD Escape Local Minima?.

Stochastic AUC Maximization with Deep Neural Networks An Alternative View: When Does SGD Escape Local Minima?

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.718224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.718224Z digest=sha256:56f0b29f13da57b4ece5e0d02bc207a35b8fc452a00e2f350afe20f22aa2a137

Observation 1ee471dc-4822-4694-919a-faa1c0df68e7 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

Stochastic AUC Maximization with Deep Neural Networks Imagenet classification with deep convolutional neural networks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.512314Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.722612Z digest=sha256:9790a29bf4315f97ebf1cbecb82a59c6eefe4e802f69c69c656cf188542e3125

Observation c8a3f5c0-3137-4d4e-b0e8-1b43d3494ed1 · outbound

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

Stochastic AUC Maximization with Deep Neural Networks Non-convex finite-sum optimization via scsg methods

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.501054Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.726430Z digest=sha256:ed3b6a499df35d39bdd83cfaca061b927fd9fcdb271ad82ca1f93e14cc4f349f

Observation e84b39d8-865b-49a6-8488-8ffc319a5cba · outbound

This paper cites Learning overparameterized neural networks via stochastic gradient descent on structured data.

Stochastic AUC Maximization with Deep Neural Networks Learning overparameterized neural networks via stochastic gradient descent on structured data

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.489499Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.730515Z digest=sha256:c4f0222d9fba08fbe9ef01162c172753f9a0a4b004522b3e254d15bb820e2ba8

Observation 636a9a10-62ec-4086-b266-1c32f77fe1b7 · outbound

This paper cites Convergence analysis of two-layer neural networks with relu activation.

Stochastic AUC Maximization with Deep Neural Networks Convergence analysis of two-layer neural networks with relu activation

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.476837Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.734533Z digest=sha256:7de4e2220d8e5df127d338bc8623f79eb2f84562235929e31b456ec75817c582

Observation 6711c80e-0176-4a41-82f4-dbaac111c419 · outbound

This paper cites A simple proximal stochastic gradient method for nonsmooth nonconvex optimization.

Stochastic AUC Maximization with Deep Neural Networks A simple proximal stochastic gradient method for nonsmooth nonconvex optimization

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.465431Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.738548Z digest=sha256:83cb069c769ba98d1c667babec67eb1c767df41657f1a3e64fd13e39a25b8bce

Observation f42fe7e5-5445-4b62-9576-4c6805fa5566 · outbound

This paper cites First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems.

Stochastic AUC Maximization with Deep Neural Networks First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.742183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.742183Z digest=sha256:a8d0ab80b3b6ca853dc19c84a51ce26e1e8a8f71f44b8eb8d158f3788a0cdbfc

Observation 0c1ade9b-5161-4b01-bbbb-bfb81ae526b7 · outbound

This paper cites Fast stochastic auc maximization with o (1/n)-convergence rate.

Stochastic AUC Maximization with Deep Neural Networks Fast stochastic auc maximization with o (1/n)-convergence rate

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.453674Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.746057Z digest=sha256:d331a12ade463cd2b105b7481eee1691174cb7c04b344dd8242a89cb5e0529cf

Observation c8be1fbb-1c74-493c-98a8-051b0f20386e · outbound

This paper cites Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications.

Stochastic AUC Maximization with Deep Neural Networks Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:41:35.122474Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.749710Z digest=sha256:fd858a1015e356a752c9b79ef4c3f96873d89fedc0b8413d019bd283fa0d0c00

Observation df21cc64-d9f0-48e8-9269-c9eacb095d20 · outbound

This paper cites Acoustic modeling using deep belief networks.

Stochastic AUC Maximization with Deep Neural Networks Acoustic modeling using deep belief networks

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.441387Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.753676Z digest=sha256:a6495fa51dae5f6a6631ea492d957604fe9e92d4b22d865eba5423e240cbfb31

Observation 37e38b5c-9b91-4b6b-b634-1da4d972733c · outbound

This paper cites Stochastic proximal algorithms for auc maximization.

Stochastic AUC Maximization with Deep Neural Networks Stochastic proximal algorithms for auc maximization

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.429524Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.757606Z digest=sha256:9a081d10c1b9bdf8a6c21063e025df1b4e1e4007fe95e6a178abf98721809607

Observation 92f9e986-4d8c-49c7-aec1-4d672567d30e · outbound

This paper cites Robust stochastic approximation approach to stochastic programming.

Stochastic AUC Maximization with Deep Neural Networks Robust stochastic approximation approach to stochastic programming

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.761445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.761445Z digest=sha256:9e20ed8af9e62b556cc83d637eb6294b2388f65478f6cfc37d5fa2363adf69d0

Observation af410f7f-a3bc-41be-af4c-342f75ef9c25 · outbound

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

Stochastic AUC Maximization with Deep Neural Networks Introductory lectures on convex optimization: A basic course, volume 87

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.765265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.765265Z digest=sha256:1ab8c5aaa0ab0d42d0cbba47c66b5e55ff6ad15f888f53538d26479962ead5e1

Observation 45d40c13-af2c-448e-99db-c15f82ab4383 · outbound

This paper cites Stochastic Recursive Gradient Algorithm for Nonconvex Optimization.

Stochastic AUC Maximization with Deep Neural Networks Stochastic Recursive Gradient Algorithm for Nonconvex Optimization

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.769327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.769327Z digest=sha256:0db6e2f7d42bcc8cd4a9f57b424fcbdaa907a14d1dcbe3590a58a48b3a8cc973

Observation da331526-1a69-4ddb-abb2-2fedc82a8ed4 · outbound

This paper cites Gradient methods for minimizing functionals.

Stochastic AUC Maximization with Deep Neural Networks Gradient methods for minimizing functionals

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.401985Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.773778Z digest=sha256:c49502c72499ead9f48b7a9249ea76a3ffca93f63753ef231fd1e0ee2d3eadb8

Observation 7a8f2219-79ec-4ae4-aa29-a825a400b4b0 · outbound

This paper cites Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.

Stochastic AUC Maximization with Deep Neural Networks Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.777456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.777456Z digest=sha256:a1e7d0e494f3d1de9f45bef7ec998ed4ad0ef68488cf8df59160ada40473a6f6

Observation 1ce85653-22a3-4f27-b4d6-468689001f76 · outbound

This paper cites Stochastic variance reduction for nonconvex optimization.

Stochastic AUC Maximization with Deep Neural Networks Stochastic variance reduction for nonconvex optimization

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.389758Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.781450Z digest=sha256:94ee2b01b7c6e5b021150ff52db0a295ab21d92eef08416176eaaf84a89222f7

Observation f47ae651-b6d9-4f56-bada-89bdeb1802c1 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Stochastic AUC Maximization with Deep Neural Networks Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.375979Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.785234Z digest=sha256:521e6de688a6b4693e7a0deb52697754b469d6807d804b8a3e765fa46974ef71

Observation 46eb98ec-99d6-470a-8c5c-115b22757251 · outbound

This paper cites Monotone operators and the proximal point algorithm.

Stochastic AUC Maximization with Deep Neural Networks Monotone operators and the proximal point algorithm

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.360813Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.789169Z digest=sha256:663deca24a889337c286acf61b03c8b4c40f8753e979acfd1d19bb98d75f7649

Observation c9fec064-8024-4d09-baf8-43f512314f9a · outbound

This paper cites Solving Non-Convex Non-Concave Min-Max Games Under Polyak-{\L}ojasiewicz Condition.

Stochastic AUC Maximization with Deep Neural Networks Solving Non-Convex Non-Concave Min-Max Games Under Polyak-{\L}ojasiewicz Condition

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.793076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.793076Z digest=sha256:fe1c023340a8e66fdc81f5100c9e8e502ae901bb4847e3695b99bfe7374d097b

Observation b93fa3ed-8362-4356-9560-9d80042840fc · outbound

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

Stochastic AUC Maximization with Deep Neural Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.797210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.797210Z digest=sha256:05a52ffb61fe42548f973b05ea74c57748567e7302bf78671c11f35a744c3b96

Observation 597bb0a1-b4da-47df-a96f-7568583ebf05 · outbound

This paper cites Sequence to sequence learning with neural networks.

Stochastic AUC Maximization with Deep Neural Networks Sequence to sequence learning with neural networks

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.348385Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.802017Z digest=sha256:9d54abeca7a05b4453c7b23397382d2057865f3ae066d972f6ab4ee6b4db845e

Observation e3dfdfc1-5f79-41d9-bcde-55de5bdff928 · outbound

This paper cites SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms.

Stochastic AUC Maximization with Deep Neural Networks SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.805848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.805848Z digest=sha256:e99c74740af3a1d1352339a6541d7d3ad46cc161f8cd6dc6a22b79c344445eb4

Observation 53f37d68-1b6d-4307-a7c2-4ba6072083e5 · outbound

This paper cites Stochastic online auc maximization.

Stochastic AUC Maximization with Deep Neural Networks Stochastic online auc maximization

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.334470Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.809805Z digest=sha256:c30e01de97cc008dfdcf1da1333fa49272644391b7d5653e9242a51730288ce0

Observation d8687003-4a0e-4f95-a393-3fc9e6fd778a · outbound

This paper cites Online auc maximization.

Stochastic AUC Maximization with Deep Neural Networks Online auc maximization

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.318341Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.813515Z digest=sha256:7a3be50efd0f871f1fdd2694711b1fb50bad8a157ddc66af5105d58391215a16

Observation 71fa6473-5068-44ad-ba3b-254e9a91f30b · outbound

This paper cites Stochastic nested variance reduced gradient descent for nonconvex optimization.

Stochastic AUC Maximization with Deep Neural Networks Stochastic nested variance reduced gradient descent for nonconvex optimization

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T10:41:35.303660Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.817273Z digest=sha256:3e901ae90de9bcbebf32bd898603013e675e09b002a7e5b186ede00fa3334744

Observation 598cf54c-9e46-4430-98ab-611d38964c93 · outbound

This paper cites Characterization of Gradient Dominance and Regularity Conditions for Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks Characterization of Gradient Dominance and Regularity Conditions for Neural Networks

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.821062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.821062Z digest=sha256:7413d501cf469484b6710609851f47b67a6302a92237b10cda2dbe1617bace24

Observation 85d72c24-c6dd-46a7-bc8b-c09c9bb2bc52 · outbound

This paper cites An Improved Analysis of Training Over-parameterized Deep Neural Networks.

Stochastic AUC Maximization with Deep Neural Networks An Improved Analysis of Training Over-parameterized Deep Neural Networks

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.825116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.825116Z digest=sha256:1f7252a077ef4639820e45956b1b5b12133144c89dbbb7622e1d4a2a07512574

Observation a0eeea83-45dc-4a6f-948d-46a4be3a185f · outbound

This paper cites Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.

Stochastic AUC Maximization with Deep Neural Networks Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.829076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.829076Z digest=sha256:ebd861a9b3a515a426ea49a85981f900be1b574d5f1c87b798cf923c32b7285c

Observation 9103c397-b9cf-4fba-90ec-5dcfa5cde6b9 · outbound

This paper cites write newline.

Stochastic AUC Maximization with Deep Neural Networks write newline

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.833067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.833067Z digest=sha256:6560c2ab05c6b20e7525186084593f66a85ccb4b904e9bcefcea4c22dde40dff

Observation 5ccb6a35-fcbd-409c-be27-5ed226185cab · outbound

This paper cites @esa (Ref.

Stochastic AUC Maximization with Deep Neural Networks @esa (Ref

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.838573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.838573Z digest=sha256:4474d017dda1147762dd5daca5d3811cea9d3749ec3b79f211e8596dda13290f

Observation c4bc5a95-2009-434d-b3e8-8d3f83af2d96 · outbound

This paper cites an unresolved cited work.

Stochastic AUC Maximization with Deep Neural Networks Unresolved cited work

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:34.843885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T10:41:34.843885Z digest=sha256:0e8874b13da2b0b08b691e41022b2924ccf762188b6652ea1316463da875f9fb

Observation 4e962bfa-7284-4710-a63e-ce504e46afa8 · outbound

This paper cites 1h A XHT J e ,..b ] K Lxb-.

Stochastic AUC Maximization with Deep Neural Networks 1h A XHT J e ,..b ] K Lxb-

Reference 54

Resolution
malformed identifier
raw_fallback, observed 2026-08-14T10:41:35.014329Z

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.

source=arxiv_source observed=2026-08-14T10:41:34.848326Z digest=sha256:e4ef018cc6e78e7df6b12ef5e03e8246c95bdbb7f7f5fa076ab8d254c2f25491

Pith citing papers

Observation 9ebce470-4ddd-437e-bf81-1325dedb47a3 · inbound

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning cites this paper.

Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning Stochastic AUC Maximization with Deep Neural Networks

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-11T05:38:21.913880Z

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.

source=pdf_text observed=2026-08-11T05:38:21.793863Z digest=sha256:12225924bc60628cfcde5004eccb0afbadcb679146cbe60f0037d50188db8c03