Results 41 to 50 of about 117,260 (178)
Applied Computer Systems, 2017 A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the
Romanuke Vadim V.
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Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process
Sensors, 2021 In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures ...
Mohsen Saffari +4 more
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A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
, 2015 This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently
Chen, Yunchuan +5 more
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Almost Sure Convergence of Dropout Algorithms for Neural Networks
arXiv, 2020 We investigate the convergence and convergence rate of stochastic training algorithms for Neural Networks (NNs) that have been inspired by Dropout (Hinton et al., 2012). With the goal of avoiding overfitting during training of NNs, dropout algorithms consist in practice of multiplying the weight matrices of a NN componentwise by independently drawn ...
Senen-Cerda, Albert, Sanders, Jaron
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Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays
IEEE Access, 2021 Automatic detection of solar array faults reduces maintenance costs and increases efficiency. In this paper, we address the problem of fault detection, localization, and classification in utility-scale photovoltaic (PV) arrays using machine learning ...
Sunil Rao +5 more
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Shakeout: A New Approach to Regularized Deep Neural Network Training
, 2018 Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper,
Kang, Guoliang, Li, Jun, Tao, Dacheng
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Altitude Training: Strong Bounds for Single-Layer Dropout [PDF]
, 2014 Dropout training, originally designed for deep neural networks, has been successful on high-dimensional single-layer natural language tasks. This paper proposes a theoretical explanation for this phenomenon: we show that, under a generative Poisson topic
Fithian, William +3 more
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A Theoretical Analysis of Deep Neural Networks for Texture Classification
, 2016 We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations.
Basu, Saikat +6 more
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Variational Dropout Sparsifies Deep Neural Networks
, 2017 Published in ICML ...
Molchanov, Dmitry +2 more
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Flipover outperforms dropout in deep learning
Visual Computing for Industry, Biomedicine, and ArtFlipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts ...
Yuxuan Liang +3 more
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