Search Results - Layered sparse autoencoder

Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle by Logapriya, E., Rajendran, Surendran, Zakariah, Mohammad

“… Next, feature extraction is accomplished using the Layered Sparse Autoencoder Network…”
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Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks by Kamimura, Ryotaro, Takeuchi, Haruhiko

“…The present paper aims to propose a new neural network called “sparse semi-autoencoder…”
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Supervised Learning via Unsupervised Sparse Autoencoder by Liu, Jianran, Li, Chan, Yang, Wenyuan

“… In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal…”
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Layered Media Parameter Inversion Method Based on Deconvolution Autoencoder and Self-Attention Mechanism Using GPR Data by Yang, Xiaopeng, Sun, Haoran, Guo, Conglong, Li, Yixuan, Gong, Junbo, Qu, Xiaodong, Lan, Tian

“… the deconvolution autoencoder and the parameter inversion network. First, the deconvolution autoencoder is introduced to solve the pulse response of layered medium systems…”
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Nonredundant sparse feature extraction using autoencoders with receptive fields clustering by Ayinde, Babajide O., Zurada, Jacek M.

“… Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading…”
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Anomaly detection for blueberry data using sparse autoencoder-support vector machine by Wei, Dianwen, Zheng, Jian, Qu, Hongchun

“… To address this issue, this article proposes a hybrid method of combining a sparse autoencoder with a support vector machine…”
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Stacked Autoencoder Based Weak Supervision for Social Image Understanding by Xu, Chaoyang, Dai, Yuanfei, Lin, Renjie, Wang, Shiping

“… are frequently utilized for this purpose. Autoencoder models have been validated to be effective in learning latent low-dimensional representations in unsupervised learning…”
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A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis by Sohaib, Muhammad, Kim, Cheol-Hong, Kim, Jong-Myon

“… A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs…”
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A Novel Group Recommendation Model With Two-Stage Deep Learning by Huang, Zhenhua, Liu, Yajun, Zhan, Choujun, Lin, Chen, Cai, Weiwei, Chen, Yunwen

“… Yet, their recommendation performance is still unsatisfactory due to sparse group-item interactions…”
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A hybrid anomaly detection method for high dimensional data by Zhang, Xin, Wei, Pingping, Wang, Qingling

“… instances from normal instances. To address this, this article proposes an anomaly detection method combining an autoencoder and a sparse weighted least squares-support vector machine…”
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Clustering of receptive fields in Autoencoders by Ayinde, Babajide O., Zurada, Jacek M.

“… The results from previous work show that a good number of encoding and decoding filters of layered autoencoders are duplicative thereby enforcing two or more processing filters to extract the same…”
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The Deep Neural Network Based Classification of Fingers Pattern Using Electromyography by Ahmad, Jawad, Butt, Ammar Mohsin, Hussain, Mohsin, Akbar, Muhammad Azeem, Rehman, Waheed Ur

“…). The hidden layer holds Sparse Autoencoders followed by a SoftMax layer that are stacked together to form multi-layered feed forward network…”
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Securing the Road Ahead: A Survey on Internet of Vehicles Security Powered by a Conceptual Blockchain‐Based Intrusion Detection System for Smart Cities by Al‐Quayed, Fatima, Tariq, Noshina, Humayun, Mamoona, Aslam Khan, Farrukh, Attique Khan, Muhammad, Alnusairi, Thanaa S.

“…ABSTRACT The Internet of Vehicles (IoV) is a critical component of the smart city. Various nodes exchange sensitive data for urban mobility, such as…”
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Hands-on deep learning algorithms with Python: master deep learning algorithms with math by implementing them from scratch by Sudharsan Ravichandiran, Ravichandiran

“… such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities…”
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Hands-On Deep Learning Algorithms with Python: Master Deep Learning Algorithms with Extensive Math by Implementing Them Using TensorFlow by Ravichandiran, Sudharsan

“…This book introduces basic-to-advanced deep learning algorithms used in a production environment by AI researchers and principal data scientists; it explains…”
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Deep metabolome: Applications of deep learning in metabolomics by Pomyen, Yotsawat, Wanichthanarak, Kwanjeera, Poungsombat, Patcha, Fahrmann, Johannes, Grapov, Dmitry, Khoomrung, Sakda

“…[Display omitted] •The applications of deep learning has recently emerged in metabolomics research.•Deep learning has been most widely applied in data…”
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Unsupervised Relational Feature Learning for Vision by Konda, Kishore Reddy

“…This thesis contributes to the field of machine learning with a specific focus on the methods for learning relations between the inputs. Learning relationships…”
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Only sparsity based loss function for learning representations by Bakaraju, Vivek, Kishore Reddy Konda

“…We study the emergence of sparse representations in neural networks. We show that in unsupervised models with regularization, the emergence of sparsity…”
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