Results 41 to 50 of about 876,297 (223)
Needle-based deep-neural-network camera [PDF]
Applied Optics, 2021 We experimentally demonstrate a camera whose primary optic is a cannula/needle ( d i a m e t e r = 0.22 m m and l e n g t h = 12.5 m m ) that acts as a ...
Ruipeng Guo, Soren Nelson, Rajesh Menon
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Deep Learning-Based Intrusion Detection With Adversaries
IEEE Access, 2018 Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image ...
Zheng Wang
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Spectrum-based deep neural networks for fraud detection
, 2017 In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as
Li, Jun +3 more
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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
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Deep neural networks in psychiatry [PDF]
Molecular Psychiatry, 2019 Machine and deep learning methods, today's core of artificial intelligence, have been applied with increasing success and impact in many commercial and research settings. They are powerful tools for large scale data analysis, prediction and classification, especially in very data-rich environments ("big data"), and have started to find their way into ...
Daniel, Durstewitz +2 more
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Efficient Deep Feature Learning and Extraction via StochasticNets
, 2015 Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul +3 more
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, 2015 Proposed in the 1940s as a simplified model of the elementary computing unit in the human cortex, artificial neural networks (ANNs) have since been an active research area. Among the many evolutions of ANN, deep neural networks (DNNs) (Hinton, Osindero, and Teh 2006) stand out as a promising extension of the shallow ANN structure.
Mariette Awad, Rahul Khanna
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A new deep neural network for forecasting: Deep dendritic artificial neural network
Artificial Intelligence Review, 2023 Abstract Deep artificial neural networks have become a good alternative to classical forecasting methods in solving forecasting problems. Popular deep neural networks classically use additive aggregation functions in their cell structures.
Egrioglu, Erol, Bas, Eren
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Towards explainable deep neural networks (xDNN) [PDF]
Neural Networks, 2020 In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires very little computational resources (no need for GPUs) and short training times (in the order of seconds).
Plamen Angelov, Eduardo Soares
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Search for deep graph neural networks
Information Sciences, 2023 Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which ...
Guosheng Feng +2 more
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