Who Cited It

Dropout: a simple way to prevent neural networks from overfitting

2014 · 34,236 citations · 57 from inside this corpus

Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov 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.

Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…Reducing the Dimensionality of Data with Neural Networks (2006)Reducing the Dimensionality o…A Fast Learning Algorithm for Deep Belief Nets (2006)A Fast Learning Algorithm for…Practical Bayesian Optimization of Machine Learning Algorithms (2012)Practical Bayesian Optimizati…Kaldi Speech Recognition Toolkit (2024)Kaldi Speech Recognition Tool…Bayesian Learning for Neural Networks (1996)Bayesian Learning for Neural …Acoustic Modeling Using Deep Belief Networks (2011)Acoustic Modeling Using Deep …Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh… (2015)Batch Normalization: Accelera…Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh… (2024)Batch Normalization: Accelera…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…Explaining and Harnessing Adversarial Examples (2014)Explaining and Harnessing Adv…Semi-Supervised Classification with Graph Convolutional Networks (2016)Semi-Supervised Classificatio…Domain-Adversarial Training of Neural Networks (2017)Domain-Adversarial Training o…Deep Learning in Medical Image Analysis (2017)Deep Learning in Medical Imag…mixup: Beyond Empirical Risk Minimization (2017)mixup: Beyond Empirical Risk …Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Mode… (2018)An Empirical Evaluation of Ge…Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning (2015)Dropout as a Bayesian Approxi…Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017)Deep Convolutional Neural Net…Gaussian Error Linear Units (GELUs) (2016)Gaussian Error Linear Units (…Deep learning for healthcare: review, opportunities and challenges (2017)Deep learning for healthcare:…Ensemble learning: A survey (2018)Ensemble learning: A surveyAn Introduction to Deep Learning for the Physical Layer (2017)An Introduction to Deep Learn…Human-level concept learning through probabilistic program induction (2015)Human-level concept learning …Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised … (2018)Virtual Adversarial Training:…Conformer: Convolution-augmented Transformer for Speech Recognition (2020)Conformer: Convolution-augmen…Methods for interpreting and understanding deep neural networks (2017)Methods for interpreting and …Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions (2018)Natural TTS Synthesis by Cond…Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (2015)Dropout as a Bayesian Approxi…SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021)SimCSE: Simple Contrastive Le…A review of uncertainty quantification in deep learning: Techniques, applications and cha… (2021)A review of uncertainty quant…A survey on semi-supervised learning (2019)A survey on semi-supervised l…Unsupervised Domain Adaptation by Backpropagation (2014)Unsupervised Domain Adaptatio…End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF (2016)All-optical machine learning using diffractive deep neural networks (2018)All-optical machine learning …Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)
36 of 42 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.
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What this paper cites, inside the corpus

PaperYearCited
Reducing the Dimensionality of Data with Neural Networks200621,247
A Fast Learning Algorithm for Deep Belief Nets200616,540
Practical Bayesian Optimization of Machine Learning Algorithms20125,674
Kaldi Speech Recognition Toolkit20244,895
Bayesian Learning for Neural Networks19964,397
Acoustic Modeling Using Deep Belief Networks20111,752

What cites it, inside the corpus

PaperYearCited
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…201524,404
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Sh…202415,663
Distilling the Knowledge in a Neural Network201514,099
Explaining and Harnessing Adversarial Examples20148,146
Semi-Supervised Classification with Graph Convolutional Networks20168,058
Domain-Adversarial Training of Neural Networks20177,702
Deep Learning in Medical Image Analysis20174,917
mixup: Beyond Empirical Risk Minimization20174,806
Membership Inference Attacks Against Machine Learning Models20174,490
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Mode…20184,364
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning20154,187
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review20173,570
Gaussian Error Linear Units (GELUs)20163,197
Deep learning for healthcare: review, opportunities and challenges20173,115
Ensemble learning: A survey20183,088
An Introduction to Deep Learning for the Physical Layer20173,005
Human-level concept learning through probabilistic program induction20152,958
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised …20182,873
Conformer: Convolution-augmented Transformer for Speech Recognition20202,867
Methods for interpreting and understanding deep neural networks20172,763
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions20182,708
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning20152,672
SimCSE: Simple Contrastive Learning of Sentence Embeddings20212,645
A review of uncertainty quantification in deep learning: Techniques, applications and cha…20212,644
A survey on semi-supervised learning20192,636
Unsupervised Domain Adaptation by Backpropagation20142,608
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF20162,595
All-optical machine learning using diffractive deep neural networks20182,553
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538

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Topics

Machine Learning and Data ClassificationComputer Science
Machine Learning and AlgorithmsComputer Science
Neural Networks and ApplicationsComputer Science

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complete

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

  • supports5 author record(s) attached.
  • supports36 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

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Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

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