Dropout: a simple way to prevent neural networks from overfitting
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- Ilya Sutskever
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- Published 2014-06 authorship evidence
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- Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research · published 2014-06 · Journal of Machine Learning Research 15, pp. 1929–1958. Located by the cross-family machine check on 2026-10-30 (item A023; Journal citation; the PDF prints “Published 6/14” on p. 1), at the precision of its source; not read again at admission.
- Dropout: a simple way to prevent neural networks from overfitting Journal of Machine Learning Research · published 2014-06 · Journal PDF; it prints “Published 6/14” on p. 1 (cross-family check, item A023). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9c196ccbe6c6a595a1adba6cd030d35f7c2e548bbf5e7f1278b0109d8dd9ebaa). The author list is the byline as printed on the first page.
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Cited by · 3
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- Batch Normalization (2015 paper)Batch Normalization (2015 paper) cites Dropout: a simple way to prevent neural networks from overfitting.2015-02-11 · publication · Stated in source · Supported Inspect claim & evidence ↗Batch Normalization (2015 paper): document page →
- ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation cites Dropout: a simple way to prevent neural networks from overfitting.2021-07-05 · publication · Stated in source · Supported Inspect claim & evidence ↗ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation: document page →
- Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionWeak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites Dropout: a simple way to prevent neural networks from overfitting.2023-12-14 · publication · Stated in source · Supported Inspect claim & evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision: document page →
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- In its own wordsThe paper presents dropout, a technique against overfitting that randomly drops units and their connections from a neural network during training.“The key idea is to randomly drop units (along with their connections) from the neural network during training.” PDF p. 1, abstract2014-06 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe authors report that dropout improved neural network performance on supervised tasks in vision, speech recognition, document classification and computational biology.“We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.” PDF p. 1, abstract2014-06 · publication · Stated in source · Supported Inspect claim & evidence ↗