Who Cited It

mixup: Beyond Empirical Risk Minimization

2017 · arXiv (Cornell University) · 4,806 citations · 6 from inside this corpus

Hongyi Zhang, Moustapha Cissé, Yann Dauphin, David López-Paz low

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

mixup: Beyond Empirical Risk Minimization (2017)mixup: Beyond Empirical Risk …[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…SMOTE: Synthetic Minority Over-sampling Technique (2002)SMOTE: Synthetic Minority Ove…Statistical Learning Theory (1999)Statistical Learning TheoryAggregated Residual Transformations for Deep Neural Networks (2017)Aggregated Residual Transform…Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou… (2012)Deep Neural Networks for Acou…Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (2015)Deep Speech 2: End-to-End Spe…A survey on semi-supervised learning (2019)A survey on semi-supervised l…FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020)FixMatch: Simplifying Semi-Su…AutoML: A survey of the state-of-the-art (2020)AutoML: A survey of the state…Unsupervised Data Augmentation for Consistency Training (2019)Unsupervised Data Augmentatio…Automated Machine Learning (2019)Automated Machine LearningNeural Architecture Search: A Survey (2018)Neural Architecture Search: A…
13 of 13 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
Dropout: a simple way to prevent neural networks from overfitting201434,236
SMOTE: Synthetic Minority Over-sampling Technique200232,402
Statistical Learning Theory199926,957
Aggregated Residual Transformations for Deep Neural Networks201712,015
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Fou…201210,399
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin20152,181

What cites it, inside the corpus

PaperYearCited
A survey on semi-supervised learning20192,636
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence20202,271
AutoML: A survey of the state-of-the-art20201,705
Unsupervised Data Augmentation for Consistency Training20191,624
Automated Machine Learning20191,451
Neural Architecture Search: A Survey20181,406

Links

DOI · Open access copy · OpenAlex record

Topics

Adversarial Robustness in Machine LearningComputer Science
Anomaly Detection Techniques and ApplicationsComputer Science
Advanced Neural Network ApplicationsComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports29 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:44+00:00.

sha256 648f8491aaceaa34…