mlsanityData to 7 Oct 2026

Analysis of dropout learning regarded as ensemble learning

arXiv 1706.06859 ↗First posted 20 Jun 2017cs.LG stat.MLcited by 15

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Abstract (arXiv)

Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers, huge number of units, and connections. Therefore, overfitting is a serious problem. To avoid this problem, dropout learning is proposed. Dropout learning neglects some inputs and hidden units in the learning process with a probability, p, and then, the neglected inputs and hidden units are combined with the learned network to express the final output. We find that the process of combining the neglected hidden units with the learned network can be regarded as ensemble learning, so we analyze dropout learning from this point of view.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2017.

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Citing papers per year: 2021: 6; 2022: 2; 2023: 0; 2024: 4; 2025: 2; 2026: 1.

15 papers.

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  1. 2021Transformers Can Do Bayesian Inferencecited by 338
  2. 2025DropoutGS: Dropping Out Gaussians for Better Sparse-view Renderingcited by 29
  3. 2021Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inferencecited by 16
  4. 2024Posterior Uncertainty Quantification in Neural Networks using Data Augmentationcited by 9
  5. 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learningcited by 5
  6. 2024Continual learning with the neural tangent ensemblecited by 4
  7. 2026SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecastingcited by 4
  8. 2024Simmering: Sufficient is better than optimal for training neural networkscited by 2
  9. 2021Exploring Dropout Discriminator for Domain Adaptationcited by 1
  10. 2022Why Neural Networks Workcited by 1
  11. 2021Classifying Tweet Sentiment Using the Hidden State and Attention Matrix of a Fine-tuned BERTweet Modelcited by 0
  12. 2021Dropout Prediction Uncertainty Estimation Using Neuron Activation Strengthcited by 0
3 more
  1. 2022Towards Multimodal Depth Estimation from Light Fieldscited by 0
  2. 2024UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0cited by 0
  3. 2025Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical systemcited by 0