The DOI matches its Crossref record; 48 of the 50 references it cites were found in a registry or an index.
The dropout learning algorithm
Open accessAbstract
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As a result, it is strong in the mid-range of weights, plays a less significant role when weights are small and gradually weakens with saturation.”
From ModDrop: Adaptive Multi-Modal Gesture Recognition (2016) · cites this paper as [52]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“It has been shown though [52], that in dropout-like [53] systems activation units of complete models produce a weighted normalized geometric mean of per-model outputs.”
From ModDrop: Adaptive Multi-Modal Gesture Recognition (2016) · cites this paper as [52]
The cited source says what the claim attributes to it.
In the citing paper
“Finally, as has been shown by [52] for dropout, the multiplier proportional to the derivative of the sigmoid activation makes the regularization effect adaptive to the magnitude of the weights.”
From ModDrop: Adaptive Multi-Modal Gesture Recognition (2016) · cites this paper as [52]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000) · Advances in industrial control · doi:10.1007/978-1-4471-0409-4_3 · Cited by 7,938
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691
Spencer, J. (1991) · Medical Entomology and Zoology · Cited by 4,310
Cox, D. R. (2008) · Springer eBooks · doi:10.1007/978-0-387-32833-1_5 · Cited by 2,687
Research works printed but not linked: 1
- Mercer, 2001, Improved upper and lower bounds for the difference an-gn, J. Math., 31
Cited by
Sorted by citations
Items 201–300 of 340
Ithapu, V. K., Ravi, S. N., & Singh, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08670 · Cited by 11
Rauhameri, A., Robiños, A., Anttalainen, O., Salpavaara, T., Rantala, J., Surakka, V., Kallio, P. J., Vehkaoja, A. T., & Müller, P. (2024) · IEEE Access · doi:10.1109/access.2024.3453496 · Cited by 7
Park, S.-H., Park, S.-H., Kim, K., & Park, S.-J. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121397 · Cited by 6
MedGaze: Gaze Estimation on WCE Images Based on a CNN Autoencoder
Dimas, G., Iakovidis, D. K., & Koulaouzidis, A. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00071 · Cited by 6
Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding
Ma, E. Y. T., Ratnasingham, S., & Kremer, S. C. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2598752 · Cited by 5
Almost Sure Convergence of Dropout Algorithms for Neural NetworksPreprint
Senen–Cerda, A., & Sanders, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02247 · Cited by 6
Variational global clue inference for weakly supervised video moment retrieval
Lv, Z., & Su, B. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113071 · Cited by 5
Hoang-Trong, D. D., Tran, K., Trieu, D.-A., Truong, Q.-H., Phan, N.-H., Phan, N.-L. T., & Le, V.-H. (2025) · Physical Review A · doi:10.1103/mckv-yrg1 · Cited by 5
Zheng, Y., Zeng, D., Tian, Y., Li, S., He, S., & Li, H. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1610520 · Cited by 5
Deep learning for ultrafast X-ray scattering and imaging with intense X-ray FEL pulses
Hu, M., Fan, J., Tong, Y., Sun, Z., & Jiang, H. (2025) · Advanced Optical Technologies · doi:10.3389/aot.2025.1546386 · Cited by 5
Phan, K. T., Maul, T., & Vu, T. T. (2016) · International Journal of Parallel Programming · doi:10.1007/s10766-016-0435-4 · Cited by 5
Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go
Li, X., Lv, Z., Liu, B., Wu, L., & Wang, Z. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/1397948 · Cited by 5
Deep learning–based neural networks for day-ahead power load probability density forecasting
Zhou, Y., Zhu, D., Chen, H., Guo, S., Xu, C., & Chang, F. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-23305-0 · Cited by 5
Javaloy, A., & Garcı́a-Mateos, G. (2020) · Applied Sciences · doi:10.3390/app10175772 · Cited by 5
Asymptotic Convergence Rate of Dropout on Shallow Linear Neural Networks
Senen–Cerda, A., & Sanders, J. (2022) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/3489048.3530965 · Cited by 5
Input dropout in product unit neural networks for stream water temperature modelling
Piotrowski, A., Napiorkowski, J. J., & Piotrowska, A. E. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126253 · Cited by 5
Deep Online Convex Optimization with Gated GamesPreprint
Balduzzi, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01952 · Cited by 10
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningPreprint
Markowitz, E., Balasubramanian, K., Mirtaheri, M., Abu-El-Haija, S., Perozzi, B., Steeg, G. V., & Galstyan, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04350 · Cited by 5
The Ebb and Flow of Deep Learning: a Theory of Local Learning.Preprint
Baldi, P., & Sadowski, P. (2015) · arXiv (Cornell University) · Cited by 5
Self-citationCIMB-MVQA: Causal intervention on modality-specific biases for medical visual question answering
Liu, B., Liu, L., Ding, J., Yang, X., Peng, W., & Liu, L. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103850 · Cited by 4
Machine learning approach for wheat variety identification using single-seed imaging
Bagherpour, H., & shamohammadi, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-35252-8 · Cited by 4
Joshi, P., Størdal, S., Lien, G., Mishra, D., & Haugom, E. (2024) · IEEE Access · doi:10.1109/access.2024.3501773 · Cited by 4
Chen, C.-C., Chan, Y.-M., & Jeong, H. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115681 · Cited by 4
Li, X., Zhang, C., Yao, L., Zhang, J., Zhang, K., Feng, H., & Yu, H. (2023) · Endoscopic Ultrasound · doi:10.1097/eus.0000000000000029 · Cited by 4
ANN uncertainty estimates in assessing fatty liver content from ultrasound data
Corso, G. M. D., Pascali, M. A., Caudai, C., Rosa, L. D., Salvati, A., Mancini, M., Ghiadoni, L., Bonino, F., Brunetto, M. R., Colantonio, S., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.021 · Cited by 4
Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images
Kumar, N., & Sandhan, T. (2023) · National Conference on Communications · doi:10.1109/ncc56989.2023.10068074 · Cited by 4
Urban Road Lane Number Mining from Low-Frequency Floating Car Data Based on Deep Learning
Li, X., Zhang, Y., Xiang, L., & Wu, T. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12110467 · Cited by 4
Chandramohan, A., & Raghukiran, N. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109038 · Cited by 4
Sharko, M., Burenko, J., & Gusarina, N. (2017) · Technology audit and production reserves · doi:10.15587/2312-8372.2017.98275 · Cited by 4
DropCircuit : A Modular Regularizer for Parallel Circuit Networks
Phan, K. T., Maul, T., Vu, T. T., & Lai, W. K. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9677-4 · Cited by 5
Face recognition with improved deep belief networks
Fan, R., & Hu, W. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2017.8393043 · Cited by 4
Dropping Classes for Deep Speaker Representation Learning
Luu, C., Bell, P., & Renals, S. J. (2020) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2020-50 · Cited by 4
Improving Neural Network Generalization by Combining Parallel Circuits with Dropout
Phan, K. T., Maul, T., Vu, T. T., & Lai, W. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46675-0_63 · Cited by 4
Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
Ithapu, V. K., Singh, V., & Johnson, S. C. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-810408-8.00020-1 · Cited by 4
Analysis on Dropout Regularization
Sum, J., & Leung, C.-S. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-36802-9_28 · Cited by 4
An ETF view of Dropout regularization
Bank, D., & Giryes, R. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.11 · Cited by 4
An Analysis of Dropout for Matrix FactorizationPreprint
Cavazza, J., Lane, C., Haeffele, B. D., Murino, V., & Vidal, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.03487 · Cited by 4
DeePhy: A Deep Learning Model to Reconstruct Phylogenetic Tree from Unaligned Nucleotide SequencesPreprint
Mahapatra, A., & Mukherjee, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.09.642239 · Cited by 4
CWBLS network and its application in portable spectral measurement
Sui, Y., Zhao, X., Liu, C., Zhao, Y., Cai, L., & Tong, Y. (2025) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2025.126329 · Cited by 3
Application of Remote Sensing Image Classification Utilising Deep Learning in Technological Domains
Tarun, S., Teja, T. P. S. P., Rana, P. P., Mehta, A., Vasudev, H., Singh, G., & Singh, S. (2024) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558312821240804183436 · Cited by 3
Chowdhury, S., Carney, A., Hamerly, G., & Speegle, G. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825311 · Cited by 3
Mohamed, K., Chaieb, E. B., Mounim, C., Chadli, H., Chadli, S., Salmi, K., Derkaoui, A., & Tahani, A. (2025) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2025.101114 · Cited by 3
On the interplay of network structure and gradient convergence in deep learning
Ithapu, V. K., Ravi, S. N., & Singh, V. (2016) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2016.7852271 · Cited by 3
Classification of Signal Versus Background in High-Energy Physics Using Deep Neural Networks
Mythili, M., Thangarajan, R., & Krishnamoorthy, N. V. (2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-32150-5_111 · Cited by 3
John, C., Sahoo, J., Sajan, I. K., Madhavan, M., & Mathew, O. K. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108169 · Cited by 4
Feigl, M., Lebiedzinski, K., Herrnegger, M., & Schulz, K. (2021) · Österreichische Wasser- und Abfallwirtschaft · doi:10.1007/s00506-021-00771-3 · Cited by 3
Deep learning models of the discrete component of the Galactic interstellar γ -ray emission
Shmakov, A., Tavakoli, M., Baldi, P., Karwin, C. M., Broughton, A., & Murgia, S. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.063018 · Cited by 3
Self-citationEfficient Image Evidence Analysis of CNN Classification ResultsPreprint
Zhou, K., & Kainz, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.01693 · Cited by 3
On the relationship between Dropout and Equiangular Tight Frames.Preprint
Bank, D., & Giryes, R. (2018) · arXiv (Cornell University) · Cited by 3
Ouassaf, M., Zekri, A., Khan, S. U., Rengasamy, K. R. R., & Alhatlani, B. Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1646784 · Cited by 2
Jain, R., Mungamuri, S. K., & Garg, P. (2025) · The Journal of Gene Medicine · doi:10.1002/jgm.70066 · Cited by 2
A generic shift-norm-activation approach for deep learning
Chen, Z., & Ho, P. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107609 · Cited by 2
Blind detection of circular image rotation angle based on ensemble transfer regression and fused HOG
Dong, W., Zhang, J., Zhou, Y., Gao, L., & Zhang, X. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.1037381 · Cited by 2
RSS-Bagging: Improving Generalization Through the Fisher Information of Training Data
Wang, J., Li, F., Li, J., Hou, C., Qian, Y., & Liang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3270559 · Cited by 2
Boundedness and Convergence of Mini-batch Gradient Method with Cyclic Dropconnect and Penalty
Jing, J., Jinhang, C., Zhang, H., & Zhang, W. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11581-5 · Cited by 2
A deep learning method to predict bacterial ADP-ribosyltransferase toxins
Zheng, D., Zhou, S., Chen, L., Pang, G., & Yang, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae378 · Cited by 2
Shi, C., Li, J., & Wu, L. (2022) · Journal of Digital Health · doi:10.55976/jdh.1202214319-24 · Cited by 2
Data Augmentation via Lévy Processes
Wager, S., Fithian, W., & Liang, P. (2016) · The MIT Press eBooks · doi:10.7551/mitpress/10761.003.0013 · Cited by 2
An Analysis of Electrocardiograms for Instantaneous Sleep Potential Determination
Ogundare, O. (2019) · The Open Biomedical Engineering Journal · doi:10.2174/1874120701913010001 · Cited by 2
Enhancing Feed-Forward Neural Network in Image Classification
Daday, M. J. A., Fajardo, A. C., & Medina, R. P. (2019) · International Conference on Computing and Big Data (ICCBD) · doi:10.1145/3366650.3366651 · Cited by 2
Supervised Deep Learning Methods for Human pre-miRNA Identification
Rezoun, A. S. M., Rezoun, A. S. M., Hasan, M. A. M., Hasan, M. A. M., Aziz, A. Z. B., & Aziz, A. Z. B. (2020) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp50017.2020.9230911 · Cited by 2
Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker
Meshgi, K., Maeda, S., Oba, S., & Ishii, S. (2017) · Neural Networks · doi:10.1016/j.neunet.2016.12.009 · Cited by 2
Zhang, X. (2020) · Springer eBooks · doi:10.1007/978-981-15-2770-8_7 · Cited by 2
Jeon, J., Han, Y. J., Park, G., Sohn, D. G., Lee, S., & Im, S. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.06.005 · Cited by 2
Non-static TinyML for ad hoc networked devices
Fragkou, E., & Katsaros, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322202-3.00015-4 · Cited by 1
Clothing image classification model using regularized multiple convolutional neural networks (RMCNN)
Shuwa, A., Muhammad, A. U., Mustapha, A. B., & Matemilola, A. O. (2024) · Science World Journal · doi:10.4314/swj.v19i2.27 · Cited by 1
A Multimodal Sentiment Analysis Model for Graphic Texts Based on Deep Feature Interaction Networks
Chang, W., & Zhang, D. (2024) · International Journal of Ambient Computing and Intelligence · doi:10.4018/ijaci.355192 · Cited by 1
Aftatah, M., Khalil, A., & Zebbara, K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14430-0_25 · Cited by 1
Tool wear monitoring based on signal feature fusion and deep learning
Zhou, L., Yugang, W., Qin, J., & Cong, X. (2026) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062261418740 · Cited by 1
Beyond fragility: physics-driven neural surrogates for seismic resilience prediction of bridges
Atkins, J., Hajializadeh, D., Iqbal, W., & Soleimani, F. (2026) · Frontiers in Built Environment · doi:10.3389/fbuil.2026.1756908 · Cited by 2
Inverse Engineered Micro‐Nano Polarized Photonics Device
Li, J., Guo, M., Li, J., Yang, D., Zhong, L., Li, S., & Chen, Y. (2026) · Laser & Photonics Review · doi:10.1002/lpor.202502550 · Cited by 1
Draft: Deep Learning in Neural Networks: An Overview
Idsia, L. (2014) · Cited by 1
Pollution Control Machine Using Artificial Intelligence And Machine Learning
Pandey, A. K., Manglik, P., & Taluja, P. (2019) · International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) · doi:10.1109/iccike47802.2019.9004288 · Cited by 1
Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model
Modarresi, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50420-5_20 · Cited by 1
Grinin, L. E., Леонидович, Г. А., & Леонидович, Г. И. (2023) · Философия и общество · doi:10.30884/jfio/2023.04.01 · Cited by 2
Deep Age Estimation Model Optimization
Chaidir, M., Abidin, T. F., Sofyan, H., & Muchtar, K. (2023) · International Workshop on Artificial Intelligence and Image Processing (IWAIIP) · doi:10.1109/iwaiip58158.2023.10462896 · Cited by 1
Khodnevych, Y. V., & Stefanyshyn, D. V. (2023) · Environmental Safety and Natural Resources · doi:10.32347/2411-4049.2023.4.170-182 · Cited by 1
ADAS Evolution: LSTM-Driven Autonomous Adaptation to Individual Driver Preferences
Wang, Y., Ota, N., Debernard, S., & Popieul, J. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.01.186 · Cited by 1
Yin, P., Qi, L., Xi, X., Zhang, B., & Qiao, H. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727331 · Cited by 1
Mihăilescu, A. (2018) · InTech eBooks · doi:10.5772/intechopen.72844 · Cited by 1
Cultural Events Classification using Hyper-parameter Optimization of Deep Learning Technique
Feng, Z., & Gani, H. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120572 · Cited by 1
Gated Recurrent Unit for Landfill Area Estimation Based on Municipal Solid Waste Prediction
(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1031.58 · Cited by 1
Reddy, K. A., Regula, T., Sharmila, K., Srinivas, P. V. V. S., & Rahman, S. Z. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280505 · Cited by 1
Convergence rates for pretraining and dropout: Guiding learning parameters using network structurePreprint
Ithapu, V. K., Ravi, S. N., & Singh, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03412 · Cited by 1
Fundamental differences between Dropout and Weight Decay in Deep NetworksPreprint
Helmbold, D. P., & Long, P. M. (2016) · arXiv (Cornell University) · Cited by 1
Dropping Networks for Transfer LearningPreprint
Neill, J. O., & Bollegala, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.08501 · Cited by 1
Masking schemes for universal marginalisersPreprint
Gautam, D., Lomelí, M., Gourgoulias, K., Thompson, D. H., & Johri, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.05895 · Cited by 1
DropClass and DropAdapt: Dropping classes for deep speaker representation learningPreprint
Luu, C., Bell, P., & Renals, S. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00453 · Cited by 1
Efficient Estimation of Influence of a Training InstancePreprint
Kobayashi, S., Yokoi, S., Suzuki, J., & Inui, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04207 · Cited by 1
Machine Learning for Inferring CO2 Fluxes: The New Metaphysics of Neural NetsPreprint
Nguyen, P., & Halem, M. (2019) · doi:10.31223/osf.io/284f5 · Cited by 1
Park, S.-H., Park, B., Kim, K., & Park, S.-J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5242883 · Cited by 1
Cell identification in whole-brain multiview images of neural activationPreprint
Paciscopi, M., Silvestri, L., Pavone, F. S., & Frasconi, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.01168
A Dropout Distribution Model on Deep Transfer Learning Networks
Li, F., Yang, H., & Wang, J. (2016) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-0557-2_43
Birtea, P., Cernăzanu-Glăvan, C., & Sisu, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05990
A dropout distribution model on deep networks
Li, F., & Yang, H. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2243971
Audio source separation with deep neural networks using the dropout algorithm
Zermini, A., Wang, W., Kong, Q., Xu, Y., & Plumbley, M. D. (2017) · Surrey Research Insight Open Access (The University of Surrey)
Ma, E. (2018) · The Atrium (University of Guelph)
Vergara, D., Hernández, S., Valdenegro-Toro, M., & Jorquera, F. (2018) · arXiv (Cornell University)
Image classification with a MSF dropout
Luo, R., Zhong, X., & Chen, E. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7172-9
SENSOR-BASED HUMAN ACTIVITY RECOGNITION USING BIDIRECTIONAL LSTM FOR CLOSELY RELATED ACTIVITIES
Pavai, A. T. (2018) · CSUSB ScholarWorks (California State University, San Bernardino)
Metadata
- Authors
- Baldi, P., & Sadowski, P.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Baldi, Pierre
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2014-05
- Volume
- 210
- Pages
- 78-122
- ISSN
- 0004-3702 (ISSN-L)
- PMC
- PMC3996711
- OpenAlex
- W2164370980
- Cited by
- 340
- References
- 50
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 39 · 2023: 38 · 2022: 19 · 2021: 40 · 2020: 45 · 2019: 31 · 2018: 26 · 2017: 21 · 2016: 24 · 2015: 10 · 2014: 11
- FWCI
- 21.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- NSF (IIS-0513376, NSF-IIS-1321053)
- NIH (LM010235)
- NIH NLM (T15 LM07443)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- dropout, neural network training, ensemble averaging, logistic function, rectified linear units, stochastic neurons, stochastic gradient descent, regularization, weight decay, sparse representation, variance minimization, co-adaptation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
