Results 41 to 50 of about 298,507 (214)
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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, 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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IEEE Access, 2023 The power system frequency is an important indicator that reflects the power system’s operating status. Through real-time detection or prediction, it can effectively ensure stable power system operation.
Wenzhuo Wang +8 more
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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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StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity
IEEE Access, 2016 Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data.
Mohammad Javad Shafiee +2 more
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Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning
Energies, 2022 As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics.
Bing Liu +3 more
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Operator compression with deep neural networks
Advances in Continuous and Discrete Models, 2022 AbstractThis paper studies the compression of partial differential operators using neural networks. We consider a family of operators, parameterized by a potentially high-dimensional space of coefficients that may vary on a large range of scales. Based on the existing methods that compress such a multiscale operator to a finite-dimensional sparse ...
Kröpfl, Fabian +2 more
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Explaining deep neural networks
, 2020 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. However, the decision-making processes of these models are generally not interpretable to users.
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