Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T15:47:44.740505Z
Paper Citation Record · LEDGER
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization
As of 16 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.00636.
A citation records a reference. It does not transfer a finding from one paper to another.
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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
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Source: cited_works
Reference resolution
44 of 44 outbound references displayed
- verified exact2
- verified fuzzy32
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 84d49087-407f-41ae-87f6-9889dd41da2c · outbound
This paper cites Fuzzy control systems: Past, present and future ,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Fuzzy control systems: Past, present and future ,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.439071Z
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 6b4b0996-bdfa-447f-a6da-6e1fcc75820e · outbound
This paper cites Implementation of evol utionary fuzzy systems,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Implementation of evol utionary fuzzy systems,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.424225Z
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 8c8319e9-7857-488c-8c34-b6b70be8ab21 · outbound
This paper cites Genetic learning and performance eva luation of interval type-2 fuzzy logic controllers,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Genetic learning and performance eva luation of interval type-2 fuzzy logic controllers,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.409049Z
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 c66cc38a-d055-4b9d-b569-d787e600638e · outbound
This paper cites Back-propagation of fuzzy s ystems as nonlinear dynamic system identifiers,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Back-propagation of fuzzy s ystems as nonlinear dynamic system identifiers,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.393809Z
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 0509032c-1d53-422a-b5c4-7bc0ebd0040a · outbound
This paper cites ANFIS: Adaptive-network-based fuzzy inf erence system,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization ANFIS: Adaptive-network-based fuzzy inf erence system,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.379056Z
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 9bf5564b-9a7d-4373-9779-ad56a464c4fb · outbound
This paper cites Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA).
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T15:47:44.925688Z
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 e0bd634c-4888-4353-b640-f7a465636d5d · outbound
This paper cites Fuzzy modeling of high-dimensional systems: co mplexity reduc- tion and interpretability improvement,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Fuzzy modeling of high-dimensional systems: co mplexity reduc- tion and interpretability improvement,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.364053Z
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 bd0eb487-5730-47bc-ac11-cb8d3ab8d02f · outbound
This paper cites A hierarchica l fused fuzzy deep neural network for data classification,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization A hierarchica l fused fuzzy deep neural network for data classification,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.348961Z
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 ae6a39e1-2b29-4ded-bc19-92b3c4d77a56 · outbound
This paper cites From minimum enclosin g ball to fast fuzzy inference system training on large datasets,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization From minimum enclosin g ball to fast fuzzy inference system training on large datasets,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.333819Z
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 b3ce0a51-cc4e-4115-aad1-17a3d84f6b9b · outbound
This paper cites A multi- criteria collaborative filtering recommender system for th e tourism domain using Expectation Maximization (EM) and PCA–ANFIS,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization A multi- criteria collaborative filtering recommender system for th e tourism domain using Expectation Maximization (EM) and PCA–ANFIS,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.318910Z
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 6f8a2684-cc6f-4b2b-b4c7-fbab92a3b032 · outbound
This paper cites Fa ult diagnosis of Tennessee Eastman process with multi-scale PC A and ANFIS,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Fa ult diagnosis of Tennessee Eastman process with multi-scale PC A and ANFIS,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.304914Z
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 91871b44-c253-45ec-9644-10942af3cb80 · outbound
This paper cites A sur vey on soft subspace clustering,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization A sur vey on soft subspace clustering,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.290772Z
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 74c50c5e-a13f-47c5-80aa-327dfe6b193f · outbound
This paper cites Enhanced soft subspace clustering integrating within-cluster and betwe en-cluster in- formation,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Enhanced soft subspace clustering integrating within-cluster and betwe en-cluster in- formation,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.275983Z
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 a97fdc1f-a033-42cb-b144-d1443dc69680 · outbound
This paper cites MET SK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression pr oblems,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization MET SK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression pr oblems,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.260708Z
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 e7be3c6e-cc24-4534-95d9-d7309a9a6d89 · outbound
This paper cites Goodfellow, Y.
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.245790Z
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 4b3e1828-ade7-4834-8b70-957cb7a65c3f · outbound
This paper cites An overview of gradient descent optimization algorithms.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization An overview of gradient descent optimization algorithms
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77e2ebd6-edc4-46ce-abee-b1321a49006a · outbound
This paper cites Large-scale machine learning with stochas tic gradient de- scent,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Large-scale machine learning with stochas tic gradient de- scent,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.231424Z
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 3808f926-8693-44cd-a254-d4872f224a8a · outbound
This paper cites On the importance of initialization and momentum in deep learning,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization On the importance of initialization and momentum in deep learning,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.215830Z
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 ddb3a60e-ab12-4483-981c-7b905290a2d1 · outbound
This paper cites Adam: A method for stochastic opt imization,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Adam: A method for stochastic opt imization,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.200444Z
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 9545ed6b-0acc-45de-8d04-c35c6596a81f · outbound
This paper cites The marginal value of adaptive gradient methods in machine lear ning,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization The marginal value of adaptive gradient methods in machine lear ning,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.184730Z
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 fe760d69-01b5-403d-961d-62b2f2085bd0 · outbound
This paper cites Improving Generalization Performance by Switching from Adam to SGD.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Improving Generalization Performance by Switching from Adam to SGD
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0e28587-c95a-4a3f-9322-b87b07081a42 · outbound
This paper cites Adaptive gradient m ethods with dynamic bound of learning rate,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Adaptive gradient m ethods with dynamic bound of learning rate,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.167592Z
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 6c031bb8-f09f-4880-b45d-d97a73289006 · outbound
This paper cites Batch normalization: Acceler ating deep network training by reducing internal covariate shift,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Batch normalization: Acceler ating deep network training by reducing internal covariate shift,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.152721Z
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 28791776-f7d6-49a0-9301-d03e2e30127c · outbound
This paper cites How do es batch nor- malization help optimization?.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization How do es batch nor- malization help optimization?
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.138619Z
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 2a1dfb3d-90a7-4b13-94f5-fffbc122c691 · outbound
This paper cites Layer Normalization.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Layer Normalization
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:47:44.654844Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e5ad131-fc2f-45c3-ae12-2ccc12969bc6 · outbound
This paper cites Revisit fuzzy neural network: Demystifying ba tch normalization and ReLU with generalized hamming network,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Revisit fuzzy neural network: Demystifying ba tch normalization and ReLU with generalized hamming network,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.123480Z
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 d7ac98c9-0820-4d72-b535-1eda2568f48d · outbound
This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:47:44.663349Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90d8b8e0-8b61-4031-9fe0-3a7e313df16c · outbound
This paper cites Group normalization,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Group normalization,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.108002Z
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 ee64d84f-9f44-4378-8ed0-a5e77c49af07 · outbound
This paper cites Adaptive mixtures of local experts,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Adaptive mixtures of local experts,
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:47:44.672352Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2226c996-e67b-477c-8f11-d1e09248d55d · outbound
This paper cites Now comes the time to defuzz ify neuro- fuzzy models,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Now comes the time to defuzz ify neuro- fuzzy models,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.081602Z
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 a48ac198-288b-4eb9-91b2-c1a0e4384158 · outbound
This paper cites Comments on ‘fun ctional equivalence between radial basis function networks and fuz zy inference systems’ [and author’s reply],.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Comments on ‘fun ctional equivalence between radial basis function networks and fuz zy inference systems’ [and author’s reply],
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.063902Z
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 f30b46b2-1b45-4f0e-9abe-a4030ded5536 · outbound
This paper cites On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T15:47:44.844891Z
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 81eac3f0-6a1d-40c5-acbb-845426d2e525 · outbound
This paper cites Mixture Models for Diverse Machine Translation: Tricks of the Trade.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Mixture Models for Diverse Machine Translation: Tricks of the Trade
Reference 33
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07801df8-6c22-4ffe-8dfa-f3200ab098f6 · outbound
This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Reference 34
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa25b4bd-1722-4274-aacf-cfffed1bcca4 · outbound
This paper cites Deep residual learni ng for image recognition,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Deep residual learni ng for image recognition,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.045850Z
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 fac5b11d-e93a-4e18-80ef-4d2ae43682e1 · outbound
This paper cites Wide Residual Networks.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Wide Residual Networks
Reference 36
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d5f0c4d-227a-4a68-b763-48fdd82e101f · outbound
This paper cites Densely connected convolutional networks,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Densely connected convolutional networks,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.030261Z
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 57ab4021-0622-46f8-ac1d-22983d5bc292 · outbound
This paper cites Generating accurate rule set s without global optimization,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Generating accurate rule set s without global optimization,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:45.013479Z
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 3c693fe7-032e-4480-a946-9b2e62def173 · outbound
This paper cites Repeated incremental pruning to produce e rror reduc- tion,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Repeated incremental pruning to produce e rror reduc- tion,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:44.996322Z
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 3865788d-6280-42d0-9a48-a96af30ae4eb · outbound
This paper cites Neuro-fuzzy and soft computing-a computational approach to learning and machin e intelli- gence,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Neuro-fuzzy and soft computing-a computational approach to learning and machin e intelli- gence,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:44.981312Z
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 ee05e979-f193-4953-a7cf-36505fba3a60 · outbound
This paper cites Multiple comparisons using rank sums,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Multiple comparisons using rank sums,
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:47:44.726610Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb27defc-d57c-49f8-81ec-9ff28e7e2572 · outbound
This paper cites Controlling the false di scovery rate: A practical and powerful approach to multiple testing,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Controlling the false di scovery rate: A practical and powerful approach to multiple testing,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:44.956275Z
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 a993b657-f29d-4f70-be3d-835d6531b10b · outbound
This paper cites On large-batch training for deep learning: Generali zation gap and sharp minima,.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization On large-batch training for deep learning: Generali zation gap and sharp minima,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T15:47:44.941906Z
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 31358dff-5880-48ec-852d-8413037cba78 · outbound
This paper cites Revisiting Small Batch Training for Deep Neural Networks.
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization Revisiting Small Batch Training for Deep Neural Networks
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T15:47:44.740505Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.