Results 31 to 40 of about 218,844 (266)
Adversarial Robustness of Deep Convolutional Neural Network-based Image Recognition Models: A Review
Leida xuebao, 2021 Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change ...
Hao SUN +4 more
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Expressivity of Deep Neural Networks
, 2022 This review paper will appear as a book chapter in the book "Theory of Deep Learning" by Cambridge University ...
Ingo Gühring +2 more
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Imbedding Deep Neural Networks
CoRR, 2022 Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem.
Andrew Corbett, Dmitry Kangin
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A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study
Sakarya University Journal of Computer and Information Sciences, 2020 Deep learning, a subfield of machine learning, has proved its efficacy on a wide range of applications including but not limited to computer vision, text analysis and natural language processing, algorithm enhancement, computational biology, physical ...
Abdullah Talha Kabakuş
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CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW
Вестник КазНУ. Серия математика, механика, информатика, 2021 Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova +3 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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Stress detection using deep neural networks
BMC Medical Informatics and Decision Making, 2020 Background Over 70% of Americans regularly experience stress. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to physiological health and psychological wellbeing.
Russell Li, Zhandong Liu
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Deep Neural Network or Dermatologist? [PDF]
, 2019 Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a
Kyle Young +4 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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