The dropout learning algorithm

Cited by (340)

Items 101–200 of 340

1. Deep neural networks for human microRNA precursor detection
   Xueming Zheng, Xingli Fu, Kaicheng Wang, Meng Wang · 2020 · BMC Bioinformatics
2. Deconfounded Image Captioning: A Causal Retrospect
   Xu Yang, Hanwang Zhang, Jianfei Cai · 2020 · arXiv (Cornell University)
3. Fine-Grained Visual Categorization by Localizing Object Parts With Single Image
   Xiangtao Zheng, Lei Qi, Yutao Ren, Xiaoqiang Lu · 2020 · IEEE Transactions on Multimedia
4. Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data
   Zaobao Liu, Yongchen Wang, Long Li, Xingli Fang, Junze Wang · 2022 · Frontiers of Structural and Civil Engineering
5. On the application of neural networks for temperature field measurements using thermochromic liquid crystals
   Sebastian Möller, Christian Resagk, Christian Cierpka · 2020 · Experiments in Fluids
6. Variational Causal Inference Network for Explanatory Visual Question Answering
   Dizhan Xue, Shengsheng Qian, Changsheng Xu · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
7. $\beta$ -Dropout: A Unified Dropout
   Lei Liu, Yuhao Luo, Xu Shen, Mingzhai Sun, Bin Li · 2019 · IEEE Access
8. Exploring the Potential of Long Short‐Term Memory Networks for Predicting Net CO 2 Exchange Across Various Ecosystems With Multi‐Source Data
   Chengcheng Huang, Wei He, Jinxiu Liu, Ngoc Tu Nguyen, Hua Yang, Yiming Lv, Hui Chen, Mengyao Zhao · 2024 · Journal of Geophysical Research Atmospheres
9. CaMIL: Causal Multiple Instance Learning for Whole Slide Image Classification
   Kaitao Chen, Shiliang Sun, Jing Zhao · 2024 · Proceedings of the AAAI Conference on Artificial Intelligence
10. To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout
    Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, Oliver J. Williams · 2015 · arXiv (Cornell University)
11. Understanding Locally Competitive Networks
    Srivastava, Rupesh Kumar, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber · 2014 · arXiv (Cornell University)
12. A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video)
    Di Chen, Yi Yu, Yiwen Zhou, Bin Peng, Yujing Wang, Shan Hu, Miao Tian, Shanshan Wan, Yuelan Gao, Ying Wang et al. · 2021 · Translational Vision Science & Technology
13. Prediction of miRNA targets by learning from interaction sequences
    Xueming Zheng, Long Chen, Xiuming Li, Ying Zhang, Shungao Xu, Xinxiang Huang · 2020 · PLoS ONE
14. Deep learning regularization techniques to genomics data
    Harouna Soumaré, Alia Benkahla, Nabil Gmati · 2021 · Array
15. A Novel Ensemble Based Reduced Overfitting Model with Convolutional Neural Network for Traffic Sign Recognition System
    Anantha Babu Shanmugavel, Vijayan Ellappan, Anand Mahendran, Murali Subramanian, Ramanathan Lakshmanan, Manuel Mazzara · 2023 · Electronics
16. Technical nuances of machine learning: implementation and validation of supervised methods for genomic prediction in plant breeding
    Alencar Xavier · 2021 · Crop Breeding and Applied Biotechnology
17. Real-time reconstruction of high energy, ultrafast laser pulses using deep learning
    Matthew Stanfield, Jordan Ott, Christopher Gardner, N. F. Beier, Deano M. Farinella, Christopher A Mancuso, Pierre Baldi, Franklin J. Dollar · 2022 · Scientific Reports
18. Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases
    Hai Wang, Shaohua Xu, Kaibin Fang, Zhangsheng Dai, Guo-Zhen Wei, Lu-Feng Chen · 2023 · Journal of bone oncology
19. Application of Adaboost-Transformer Algorithm for Lithology Identification Based on Well Logging Data
    Youzhuang Sun, Shanchen Pang, Yong-An Zhang · 2024 · IEEE Geoscience and Remote Sensing Letters
20. Adaptive Dropout Method Based on Biological Principles
    Hailiang Li, Jian Weng, Yijun Mao, Yonghua Wang, Zhan Yiju, Qingling Cai, Wanrong Gu · 2021 · IEEE Transactions on Neural Networks and Learning Systems
21. A Federated Learning Approach to Tumor Detection in Colon Histology Images
    Gozde N. Gunesli, Mohsin Bilal, Shan E Ahmed Raza, Nasir Rajpoot · 2023 · Journal of Medical Systems
22. Learning in the machine: To share or not to share?
    Jordan Ott, Erik J. Linstead, Nicholas LaHaye, Pierre Baldi · 2020 · Neural Networks
23. A novel PINNs based surrogate model for multi-objective reliability-based design optimization of airfoil-shaped printed circuit heat exchangers
    Yang Li, Detao Wan, Rongdong Wang, Bingyu Ni, Zhonghua Wang, Dean Hu · 2025 · International Communications in Heat and Mass Transfer
24. Global optimization issues in deep network regression: an overview
    Laura Palagi · 2018 · Journal of Global Optimization
25. Soil temperature prediction based on explainable artificial intelligence and LSTM
    Qingtian Geng, Leilei Wang, Qingliang Li · 2024 · Frontiers in Environmental Science
26. Prediction of seakeeping in the early stage of conventional monohull vessels design using artificial neural network
    Pablo Romero-Tello, José Enrique Gutiérrez Romero, B. Serv..n-Camas · 2022 · Journal of Ocean Engineering and Science
27. Learning to identify electrons
    Julian Collado, Jessica N. Howard, Taylor Faucett, Tony Tong, Pierre Baldi, Daniel Whiteson · 2021 · Physical review. D/Physical review. D.
28. Optimal distributed parallel algorithms for deep learning framework Tensorflow
    Yuanlun Xie, Majun He, Tingsong Ma, Wenhong Tian · 2021 · Applied Intelligence
29. Dynamic artificial neural network model for ultralow temperature prediction in hydrogen storage tank
    Haeun Lee, Kiwan Kim, Daeyoung Kong, Min Hyeong Ahn, Dong‐Hoon Lee, Hobyung Jun, Chirag R. Kharangate, Jaiyoung Ryu, Hyoungsoon Lee · 2023 · Journal of Energy Storage
30. Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges
    Kristian Gundersen, Guttorm Alendal, Anna Oleynik, Nello Blaser · 2020 · Algorithms
31. A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model
    Xiran Peng, Tao Zhu, Guo Chen, Yaqiang Wang, Xuechao Hao · 2022 · Frontiers in Surgery
32. Vgg-16 And Vgg-19 Architecture Models In Lie Detection Using Image Processing
    Dewi Kusumawati, Amil Ahmad Ilham, Andani Achmad, Ingrid Nurtanio · 2022 · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)
33. Palm bunch grading technique using a multi-input and multi-label convolutional neural network
    Pronthep Pipitsunthonsan, Liangrui Pan, Shaoliang Peng, Thanate Khaorapapong, Sutkhet Nakasathien, Sittiporn Channumsin, Mitchai Chongcheawchamnan · 2023 · Computers and Electronics in Agriculture
34. Mobile and Digitally-Mediated Publishing Strategies in China: An Overview of Evolving Business Models
    Ying Peng · 2016 · Publishing Research Quarterly
35. Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction
    Lina Gong, Shujuan Jiang, Qiao Yu, Li Jiang · 2019 · IEICE Transactions on Information and Systems
36. Tuning machine learning dropout for subsurface uncertainty model accuracy
    Eduardo Maldonado-Cruz, Michael J. Pyrcz · 2021 · Journal of Petroleum Science and Engineering
37. Comments on: A random forest guided tour
    Stefan Wager · 2016 · Test
38. PASTS: Progress-aware spatio-temporal transformer speaker for vision-and-language navigation
    Liuyi Wang, Chengju Liu, Zongtao He, Shu Li, Qingqing Yan, Huiyi Chen, Qijun Chen · 2023 · Engineering Applications of Artificial Intelligence
39. GeoAI Reproducibility and Replicability: A Computational and Spatial Perspective
    Wenwen Li, Chia-Yu Hsu, Sizhe Wang, Peter Kedron · 2024 · Annals of the American Association of Geographers
40. Surprising properties of dropout in deep networks
    David P. Helmbold, Philip M. Long · 2016 · arXiv (Cornell University)
41. Correction to: Computer-aided diagnosis (CAD) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images
    Ibtissam Bakkouri, Karim Afde · 2020 · Multimedia Tools and Applications
42. Failure Analysis and Intelligent Identification of Critical Friction Pairs of an Axial Piston Pump
    Yong Zhu, Tao Zhou, Shengnan Tang, Shouqi Yuan · 2023 · Journal of Marine Science and Engineering
43. Improving predictive uncertainty estimation using Dropout–Hamiltonian Monte Carlo
    Sergio Hernández, Diego Vergara, Matías Valdenegro-Toro, Felipe Jorquera · 2019 · Soft Computing
44. Pig Identification Using Deep Convolutional Neural Network Based on Different Age Range
    Thavisack Sihalath, Jayanta Kumar Basak, Anil Bhujel, Elanchezhian Arulmozhi, Byeong Eun Moon, Hyeon Tae Kim · 2021 · Journal of Biosystems Engineering
45. CSRS: A Chinese Seal Recognition System With Multi-Task Learning and Automatic Background Generation
    Zhenyu Wang, Jie Lian, Chunfeng Song, Wei Zheng, Shaolong Yue, Senrong Ji · 2019 · IEEE Access
46. A new approach to theoretical investigations of high harmonics generation by means of fs laser interaction with overdense plasma layers. Combining particle-in-cell simulations with machine learning.
    Andreea Mihăilescu · 2016 · Journal of Instrumentation
47. Biomedical image classification using deep convolutional neural networks – overview
    M Bharath Simha Reddy, Pooja Pandey Rana · 2021 · IOP Conference Series Materials Science and Engineering
48. Brain Tumor Segmentation from Optimal MRI Slices Using a Lightweight U-Net
    Fernando Daniel Hernandez-Gutierrez, Eli Gabriel Avina-Bravo, Daniel F. Zambrano-Gutierrez, Oscar Almanza-Conejo, Mario-Alberto Ibarra-Manzano, José Ruiz-Pinales, Emmanuel Ovalle-Magallanes, Juan Gabriel Avina‐Cervantes · 2024 · Technologies
49. Ensemble learning reveals dissimilarity between rare-earth transition-metal binary alloys with respect to the Curie temperature
    Duong‐Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, Hieu‐Chi Dam · 2019 · Journal of Physics Materials
50. On better training the infinite restricted Boltzmann machines
    Xuan Peng, Xunzhang Gao, Xiang Li · 2018 · Machine Learning
51. Towards the use of deep generative models for the characterization in size of aggregated TiO2 nanoparticles measured by Scanning Electron Microscopy (SEM)
    Loïc Coquelin, Nicolas Fischer, Nicolas Feltin, Laurent Devoille, Ghazi Felhi · 2019 · Materials Research Express
52. EA3: A softmax algorithm for evidence appraisal aggregation
    Francesco De Pretis, Jürgen Landes · 2021 · PLoS ONE
53. An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN
    Yang Wang, Yi He, Sheng Yao, Lifeng Zhang, Shengpeng Cao, Zhiqing Wen · 2022 · Sensors
54. Prediction of Froth Flotation Performance Using Convolutional Neural Networks
    A. Jahedsaravani, Mohammad Massinaei, Majid Zarie · 2023 · Mining Metallurgy & Exploration
55. Forensic dental age estimation with deep learning: a modified xception model for panoramic X-Ray images
    Ercument Yilmaz, Cansu Görürgöz, Hatice Cansu Kış, Emin Murat Canger, Bengi Öztaş · 2025 · Forensic Science Medicine and Pathology
56. Robust machine learning modeling for predictive control using Lipschitz-Constrained Neural Networks
    Wallace Gian Yion Tan, Zhe Wu · 2023 · Computers & Chemical Engineering
57. Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning
    Yong Jin Park, Ji Hoon Bae, Muheon Shin, Seung Hyup Hyun, Young Seok Cho, Yearn Seong Choe, Joon Young Choi, Kyung-Han Lee, Byung‐Tae Kim, Seung Hwan Moon · 2019 · Nuclear Medicine and Molecular Imaging
58. Artificial Intelligence for Sport Actions and Performance Analysis using Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM)
    Wilton Fok, Louis C. W. Chan, Carol Chen · 2018 · Proceedings of the 2018 4th International Conference on Robotics and Artificial Intelligence
59. Text Normalization Using Encoder–Decoder Networks Based on the Causal Feature Extractor
    Adrián Javaloy, Ginés Garcı́a-Mateos · 2020 · Applied Sciences
60. Detecting pulmonary Coccidioidomycosis with deep convolutional neural networks
    Jordan Ott, David S. Bruyette, Cody Arbuckle, Dylan Balsz, Silke Hecht, Lisa F. Shubitz, Pierre Baldi · 2021 · Machine Learning with Applications
61. Faster Neural Network Training with Approximate Tensor Operations
    Menachem Adelman, Kfir Yehuda Levy, Ido Hakimi, Mark Silberstein · 2018 · arXiv (Cornell University)
62. Machine learning insights into catalyst composition and structural effects on CH4 selectivity in iron-based fischer tropsch synthesis
    Yujun Liu, Xiaolong Zhang, Luotong Li, Xingchen Liu, Tingyu Lei, Jiawei Bai, Wenping Guo, Yuwei Zhou, Xingwu Liu, Botao Teng et al. · 2024 · Artificial Intelligence Chemistry
63. Multi-Source Transfer Learning for zero-shot Structural Damage Detection
    Mohammad Hesam Soleimani‐Babakamali, Roksana Soleimani-Babakamali, Arash Kashfi Yeganeh, Kourosh Nasrollahzadeh, Onur Avcı, Serkan Kiranyaz B, Ertuǧrul Taciroğlu · 2024 · Applied Soft Computing
64. Can convolutional neural networks identify external carotid artery calcifications?
    John Nelson, Anusha Vaddi, Aditya Tadinada · 2023 · Oral Surgery Oral Medicine Oral Pathology and Oral Radiology
65. Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge
    Michael L. Biehl, Peter Sadowski, Gyan V Bhanot, Erhan Bilal, Adel Dayarian, Pablo Meyer, Raquel Norel, Kahn Rhrissorrakrai, Michael D. Zeller, Sahand Hormoz · 2014 · Bioinformatics
66. Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model
    Ryan Thomas Philips, Karishma Chhabria, V. Srinivasa Chakravarthy · 2016 · Frontiers in Neural Circuits
67. Classification of Malaria-Infected Cells using Convolutional Neural Networks
    Katarina Mitrović, Danijela Milošević · 2021 · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI)
68. The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise
    Noah Frazier-Logue, Stephen José Hanson · 2020 · Neural Computation
69. Deconfounded multi-organ weakly-supervised semantic segmentation via causal intervention
    Kaitao Chen, Shiliang Sun, Youtian Du · 2024 · Information Fusion
70. An AI based classifier model for lateral pillar classification of Legg–Calve–Perthes
    Zafer Soydan, Yavuz Sağlam, Sefa Key, Yusuf Alper Katı, Murat Taşkıran, Seyfullah Kiymet, Tuba Salturk, Ahmet Serhat Aydın, Fuat Bilgili, Cengiz Şen · 2023 · Scientific Reports
71. TranNas-NirCR: A method for improving the diagnosis of asymptomatic wheat scab with transfer learning and neural architecture search
    Xiu Jin, Jianghui Xiong, Yuan Rao, Tong Zhang, Wenjing Ba, Shangfeng Gu, Xiaodan Zhang, Jie Lu · 2023 · Computers and Electronics in Agriculture
72. Convolution neural network and deep learning
    Mubarak Taiwo Mustapha, İlker Özşahin, Dilber Uzun Ozsahin · 2024 · Elsevier eBooks
73. Efficient antihydrogen detection in antimatter physics by deep learning
    Peter Sadowski, B. Radics, A. Ananya, Y. Yamazaki, Pierre Baldi · 2017 · Journal of Physics Communications
74. Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network
    Barasha Mali, Shahedul Haque Laskar · 2020 · SN Applied Sciences
75. A self-organizing deep belief network based on information relevance strategy
    Ying-Xu Wang, Honggui Han, Min Guo, Junfei Qiao · 2019 · Neurocomputing
76. QBDC: Query by dropout committee for training deep supervised architecture
    Mélanie Ducoffe, Fŕed́eric Precioso · 2015 · arXiv (Cornell University)
77. New Deep Learning Network (Deep Residual Shrinkage Network) Is Applied for Lithology Identification To Search for the Reservoir of CO2 Geological Storage
    Youzhuang Sun, Junhua Zhang, Yongan Zhang · 2024 · Energy & Fuels
78. Early prediction of maize resistance to nicosulfuron using hyperspectral imaging and deep learning: Method and mechanism
    Tianpu Xiao, Yang Li, Dongxing Zhang, Tao Cui, Liangju Wang, Zhaohui Du, Chunji Xie, Zhimin Li, Chaoyu Gong, Hongsheng Li et al. · 2024 · Computers and Electronics in Agriculture
79. Efficient Estimation of Influence of a Training Instance
    Sosuke Kobayashi, Sho Yokoi, Jun Suzuki, Kentaro Inui · 2020 · Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)
80. Application of machine learning to characterize gas hydrate reservoirs in Mackenzie Delta (Canada) and on the Alaska north slope (USA)
    Leebyn Chong, Harpreet Singh, C. Gabriel Creason, Yongkoo Seol, Evgeniy M. Myshakin · 2022 · Computational Geosciences
81. A numerical control machining tool path step error prediction method based on BP neural network
    Ziyu Zhang, Wei Liu, Pengfei Li, Jiaping Zhang, Lv-Yang Fan · 2023 · Scientific Reports
82. Double-kernel based Bayesian approximation broad learning system with dropout
    Tao Chen, Lijie Wang, Yang Liu, Chuoqi Chen · 2024 · Neurocomputing
83. Deep Ensemble Learning for Rapid Large-Scale Postearthquake Damage Assessment: Application to Satellite Images from the 2023 Türkiye Earthquakes
    Mohammad Hesam Soleimani‐Babakamali, Mohammad Askari, Mohammad Ali Heravi, Rafet Şişman, Nahid Attarchian, Ayşegül Askan, Rojiar Soleimani, Ertuǧrul Taciroğlu · 2025 · ASCE OPEN Multidisciplinary Journal of Civil Engineering
84. Learning in the machine: Recirculation is random backpropagation
    Pierre Baldi, Peter Sadowski · 2018 · Neural Networks
85. Integrating modeled environmental variability into neural network training for underwater source localization
    Pedro Diniz, Rogério M. Calazan · 2023 · The Journal of the Acoustical Society of America
86. A deep learning–based system for mediastinum station localization in linear EUS (with video)
    Liwen Yao, Chenxia Zhang, Bo Xu, Shanshan Yi, Juan Li, Xiangwu Ding, Honggang Yu · 2023 · Endoscopic Ultrasound
87. A double-branch convolutional neural network model for species identification based on multi-modal data
    Yuxin Sun, Ye Tian, Yiyi Zhang, Mengting Yu, Xiaoquan Su, Qi Wang, Jinjia Guo, Lu Yuan, Lihui Ren · 2024 · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy
88. Some Insights Into Convolutional Neural Networks
    Junhai Zhai, Liguang Zang, Sufang Zhang · 2017 · International Conference on Machine Learning and Cybernetics
89. Dropout training for SVMs with data augmentation
    Ning Chen, Jun Zhu, Jianfei Chen, Ting Chen · 2017 · Frontiers of Computer Science
90. Deep Learning in the Natural Sciences: Applications to Physics
    Peter Sadowski, Pierre Baldi · 2018 · Lecture notes in computer science
91. Infinite Dropout for training Bayesian models from data streams
    Van-Son Nguyen, Nguyen Duc-Tung, Linh Ngo Van, Khoat Quang Than · 2019 · IEEE International Conference on Big Data (BigData)
92. Learning to isolate muons
    Julian Collado, Kevin Bauer, Edmund Witkowski, Taylor Faucett, Daniel Whiteson, Pierre Baldi · 2021 · Journal of High Energy Physics
93. Bootstrap-Based Regularization for Low-Rank Matrix Estimation
    Julie Josse, Stefan Wager · 2014 · arXiv (Cornell University)
94. Rectified Factor Networks
    Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter · 2015 · arXiv (Cornell University)
95. Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split
    Chao Chen, Zhengliang Huang, Xuyan Zou, Sheng Li, Di Zhang, Shoulin Wang · 2024 · The Science of The Total Environment
96. Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
    Sam Khallaghi, Rahebeh Abedi, Hanan Abou Ali, Hamed Alemohammad, Mary Dziedzorm Asipunu, Ismail Alatise, Nguyen Ha, Boka Luo, Cat Mai, Lei Song et al. · 2025 · Remote Sensing
97. Linear guided autoencoder: Representation learning with linearity
    Hiroshi Ohno · 2017 · Applied Soft Computing
98. Adaptive infinite dropout for noisy and sparse data streams
    Ha Nguyen, Hoang Pham, Son Nguyen, Linh Ngo Van, Khoat Quang Than · 2022 · Machine Learning
99. Semi-automated nasal PAP mask sizing using facial photographs
    Benjamin Johnston, Alistair Lee McEwan, Philip de Chazal · 2017 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
100. NOMA-Aided Probabilistic Cooperative Transmission for PC5-Based Cellular V2X Mode 4 for Crash Warning
     Takeshi Hirai, Tutomu Murase · 2021 · IEEE Access