Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction by Qingchao Zhang, Xiaojing Ye, Yunmei Chen

“…The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. …”
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M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders by Conghui Lu, Peng Chen, Hua Zhong, Mengyuan Wang

“…The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from the modulation signals, and a softmax classifier. The features learned by the stacked DSAE were then used to train the softmax classifier to demodulate the received signals into M classes. …”
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Chromosome 19p13.3 deletion in a child with Peutz-Jeghers syndrome, congenital heart defect, high myopia, learning difficulties and dysmorphic features: clinical and molecular char... by Josiane Souza, Fábio Faucz, Vanessa Sotomaior, Aguinaldo Bonalumi Filho, Jill Rosenfeld, Salmo Raskin

“…This is a report on a girl with PJS features, learning difficulties, dysmorphic features and cardiac malformation, bearing a de novo 1.1 Mb deletion at 19p13.3. …”
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Robustness may be at odds with accuracy by Tsipras, Dimitris, Santurkar, Shibani (Shibani Vinay), Engstrom, Logan G., Turner, Alexander M., Madry, Aleksander

“…These differences, in particular, seem to result in unexpected benefits: the features learned by robust models tend to align better with salient data characteristics and human perception.…”
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A novel NOx prediction model using the parallel structure and convolutional neural networks for a coal‐fired boiler by Nan Li, Yong Hu

“…The model inputs are processed and passed into three parallel subnetworks with well‐designed building blocks. The features learned by the three subnetworks are fused and used to predict NOx emissions from a 330‐MW pulverized coal‐fired utility boiler. …”
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Progress in Blind Image Quality Assessment: A Brief Review by Pei Yang, Jordan Sturtz, Letu Qingge

“…Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. …”
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Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics by Conor Keogh, James J. FitzGerald

“…We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. …”
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Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning by Feng Li, Feng Li, Zheng Zhang, Lingling Wang , Wei Liu

“…Finally, the heart sound signal is classified into different categories according to the features learned by the neural network. This paper presents comprehensive analyses of different network parameters and network connection strategies. …”
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A Transfer Learning-Based Pairwise Information Extraction Framework Using BERT and Korean-Language Modification Relationships by Hanjo Jeong

“…In this paper, we introduce an end-to-end information extraction framework comprising three key components: (1) a tagging scheme that effectively represents detailed characteristics; (2) a BERT-based transfer learning model designed for extracting named-entity tags, utilizing both general linguistic features learned from a large corpus and the sequence and symmetric-dependency features of the named-entity tags; and (3) a pairwise information extraction algorithm that pairs features with their corresponding symmetric modifying words to extract detailed information.…”
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Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics by Keogh, C, Fitzgerald, J

“…We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. …”

A spiking half-cognitive model for classification by Christian R. Huyck, Ritwik Kulkarni

“…An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. …”
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Research on the risk of block chain technology in Internet finance supported by wireless network by Yu Chen, Yayun Zhang, Bo Zhou

“…A feature combination method based on enhanced feature selection and classification is proposed according to the different features learned by each layer of the model. Combining block chain cryptography technology, distributed technology, consensus accounting mechanism of technology innovation, transaction data encapsulation into specific format data unit, encapsulated into a linear list in chronological order, using encryption algorithm trading transparency, traceability of data, security, credibility, and uniqueness in financial data analysis. …”
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Knowledge Tracing Model Based on Multiple Behavior Features Embedded Memory Networks by Bugui HE, Yongquan DONG, Rui JIA, Jiayong JIN

“…Methods First, two major features, learning and forgetting, are extracted from the interaction records, and then the extracted learning features are embedded into the memory network by scalar crossover, while the forgetting features are embedded by vector combination, which is used to enhance the learning ability of MFKT model for the students’ interaction sequences. …”
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Superpixel guided deep-sparse-representation learning for hyperspectral image classification by Fan, Jiayuan, Chen, Tao, Lu, Shijian

“…Finally, the representations (features) learned from the multiple-layer network are aggregated and trained by a support vector machine classifier. …”
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Evaluating Impacts between Laboratory and Field-Collected Datasets for Plant Disease Classification by Gianni Fenu, Francesca Maridina Malloci

“…Experiments show that model performance drops drastically when using representative datasets, and the features learned from the network to determine the class do not always belong to the leaf lesion. …”
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Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures by An Su, Chengwei Zhang, Yuan-Bin She, Yun-Fang Yang

“…In addition, an unsupervised visualization algorithm was used to interpret the molecular features learned by the deep learning models.…”
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Online multiple object tracking with enhanced Re‐identification by Wenyu Yang, Yong Jiang, Shuai Wen, Yong Fan

“…However, different tasks require to focus different features. Learning two different tasks in the same model extracted features can lead to competition between the two tasks, making it difficult to achieve optimal performance. …”
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A Visual Enhancement Network with Feature Fusion for Image Aesthetic Assessment by Xin Zhang, Xinyu Jiang, Qing Song, Pengzhou Zhang

“…Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. …”
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RGCLN: Relational Graph Convolutional Ladder-Shaped Networks for Signed Network Clustering by Anping Song, Ruyi Ji, Wendong Qi, Chenbei Zhang

“…Based on the node features learned by this end-to-end trained model, RGCLN performs community detection in a large number of real-world networks and generative networks, and the results indicate that our model has an advantage over state-of-the-art network embedding algorithms.…”
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Multi-resolution attention convolutional neural network for crowd counting by Zhang, Youmei, Zhou, Chunluan, Chang, Faliang, Kot, Alex Chichung

“…Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. …”
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