On foveation of deep neural networks by Srivastava, Sanjana.

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Dissection of Deep Neural Networks by Bau, David

“…We investigate the role of neurons within the internal computations of deep neural networks for computer vision. We introduce network dissection, a method for quantifying the alignment between human-interpretable visual concepts and individual neurons in a deep network. …”
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Explaining deep neural networks by Camburu, OM

“…<p>Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. …”

Wide deep neural networks by Hayou, S

“…<p>Deep neural networks have had tremendous success in a wide range of applications where they achieve state of the art performance. …”

Efficient Processing of Deep Neural Networks by Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer

Intuitionistic Fuzzy Deep Neural Network by Krassimir Atanassov, Sotir Sotirov, Tania Pencheva

Subjects: “…intuitionistic fuzzy deep neural network…”
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Landscape Classification with Deep Neural Networks by Daniel Buscombe, Andrew C. Ritchie

“…If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. …”
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Probabilistic Models with Deep Neural Networks by Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón

“…In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.…”
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Oscillator Simulation with Deep Neural Networks by Jamshaid Ul Rahman, Sana Danish, Dianchen Lu

Subjects: “…deep neural network…”
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Methods for Pruning Deep Neural Networks by Sunil Vadera, Salem Ameen

“…This paper presents a survey of methods for pruning deep neural networks. It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that utilise clustering to identify redundancy, and methods that use sensitivity analysis to assess the effect of pruning. …”
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Advancements in On-Device Deep Neural Networks by Kavya Saravanan, Abbas Z. Kouzani

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Deep neural networks for choice analysis by Wang, Shenhao.

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Scene parsing with deep neural networks by Ding, Henghui

“…We address scene parsing based on deep neural networks and explore to enhance scene parsing performance from different aspects. …”
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Geometry estimation by deep neural network by Mei, Jianhan

“…In this thesis, we target on constructing deep neural network for the application of 6 Dof (6D) pose estimation. …”
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Deep neural networks for stock forecasting by Gao, Kexin

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Visualizing interpretations of deep neural networks by Tan, Ryan Kang Wei

“…Deep neural networks are notoriously black boxes that defy human interpretations. …”
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Scene segmentation with deep neural networks by Shuai, Bing

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Safety verification of deep neural networks by Huang, X, Kwiatkowska, M, Wang, S, Wu, M

“…Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. …”

Concolic testing for deep neural networks by Sun, Y, Wu, M, Ruan, W, Huang, X, Kwiatkowska, M, Kroening, D

“…We develop the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method to perform concolic testing to increase test coverage. …”

Ontology reasoning with deep neural networks by Hohenecker, P, Lukasiewicz, T

“…In this paper, we employ state-of-the-art methods for training deep neural networks to devise a novel model that is able to learn how to effectively perform logical reasoning in the form of basic ontology reasoning. …”