Results 41 to 50 of about 2,238,851 (367)
Far-field super-resolution ghost imaging with a deep neural network constraint
Light: Science & Applications, 2022 Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing.
Fei Wang +6 more
semanticscholar +1 more source
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
openaire +2 more sources
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
openaire +3 more sources
Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks
Electronic Research Archive, 2023 Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets ...
Xite Yang +4 more
doaj +1 more source
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
Sally Shrapnel +4 more
openaire +4 more sources
PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method [PDF]
Geophysical Journal International, 2018 As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to earthquake monitoring.
Weiqiang Zhu, G. Beroza
semanticscholar +1 more source
Deep Neural Network Approximation Theory [PDF]
IEEE Transactions on Information Theory, 2021 IEEE Transactions on Information Theory, 67 (5)
Elbrächter, Dennis +3 more
openaire +4 more sources
A survey of efficient deep neural network
Dianxin kexue, 2020 Recently,deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure,DNN’s performance is rapidly increasing.However,deeper and lager deep
Rui MIN
doaj +2 more sources
A greenhouse modeling and control using deep neural networks
Applied Artificial Intelligence, 2021 Deep learning approaches have attracted a lot of interest and competition in a variety of fields. The major goal is to design an effective deep learning process in automatic modeling and control field.
Latifa Belhaj Salah, Fathi Fourati
doaj +1 more source
Adam Optimization Algorithm for Wide and Deep Neural Network
Knowledge Engineering and Data Science, 2019 The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning.
Imran Khan Mohd Jais +2 more
doaj +1 more source