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Measuring code maintainability with deep neural networks
The maintainability of source code is a key quality characteristic for software quality. Many approaches have been proposed to quantitatively measure...
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DyPipe: A Holistic Approach to Accelerating Dynamic Neural Networks with Dynamic Pipelining
Dynamic neural network (NN) techniques are increasingly important because they facilitate deep learning techniques with more complex network...
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Neural Networks
Though neural networks have been around for many years, because of technological advancement and computational power, they have gained popularity...
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Deep neural network-based secure healthcare framework
Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as...
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Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems....
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Fpga-based SoC design for real-time facial point detection using deep convolutional neural networks with dynamic partial reconfiguration
Deep convolutional neural networks (DCNNs) have been mainly powerful and important artificial intelligence techniques, which are exploited in various...
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A novel learning approach in deep spiking neural networks with multi-objective optimization algorithms for automatic digit speech recognition
Here, a new layered spiking neural network (SNN) learning framework is proposed using optimization algorithms for rapid and efficient pattern...
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Multiple features-based adverse drug reaction detection from social media using deep convolutional neural networks (DCNN)
Adverse drug responses (ADRs) are unfavourable side effects of using a medication that result from the medication's pharmacological activity. Social...
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Corporate Credit Ratings Based on Hierarchical Heterogeneous Graph Neural Networks
In order to help investors understand the credit status of target corporations and reduce investment risks, the corporate credit rating model has...
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Protecting IP of deep neural networks with watermarking using logistic disorder generation trigger sets
As deep learning technology matures, it’s being widely deployed in fields like image classification and speech recognition. However, training a...
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Graph neural networks for text classification: a survey
Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models...
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Biomimetic oculomotor control with spiking neural networks
Spiking neural networks (SNNs) are comprised of artificial neurons that, like their biological counterparts, communicate via electrical spikes. SNNs...
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Heterogeneous gradient computing optimization for scalable deep neural networks
Nowadays, data processing applications based on neural networks cope with the growth in the amount of data to be processed and with the increase in...
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A methodological framework for optimizing the energy consumption of deep neural networks: a case study of a cyber threat detector
The growing prevalence of deep neural networks (DNNs) across various fields raises concerns about their increasing energy consumption, especially in...
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Accelerate distributed deep learning with cluster-aware sketch quantization
Gradient quantization has been widely used in distributed training of deep neural network (DNN) models to reduce communication cost. However,...
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BestOf: an online implementation selector for the training and inference of deep neural networks
Tuning and optimising the operations executed in deep learning frameworks is a fundamental task in accelerating the processing of deep neural...
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Affective image recognition with multi-attribute knowledge in deep neural networks
Incorporating visual attributes such as objects and scene features into deep models has been proved valuable for affective image recognition. In...
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DeepCONN: patch-wise deep convolutional neural networks for the segmentation of multiple sclerosis brain lesions
Segmentation is a critical process for examining Multiple Sclerosis (MS) brain lesions for diagnosis, follow-up, and prognosis of the disease. The...
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How does a kernel based on gradients of infinite-width neural networks come to be widely used: a review of the neural tangent kernel
The neural tangent kernel (NTK) was created in the context of using the limit idea to study the theory of neural network. NTKs are defined from...
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CottonLeafNet: cotton plant leaf disease detection using deep neural networks
India is a cover crop region whereby agricultural production sustains a substantial proportion of the populace and upon which the whole Indian...
