Results 51 to 60 of about 876,297 (223)

Deep Learning for Forecasting Stock Returns in the Cross-Section

, 2018
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition ...
A Subrahmanyam, AJ Smola, C Krauss, CR Harvey, D Olson, GS Atsalakis, I Goodfellow, L Breiman, L Kryzanowski, Q Cao, RD McLean, S Soni, Y LeCun   +12 more
core   +1 more source

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, Maier, Roland, Peterseim, Daniel   +2 more
openaire   +3 more sources

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.
openaire   +2 more sources

Monkeypox detection using deep neural networks

BMC Infectious Diseases, 2023
Abstract Background In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around
Azar, Amir Sorayaie, Naemi, Amin, Rikan, Samin Babaei, Mohasefi, Jamshid Bagherzadeh, Pirnejad, Habibollah, Wiil, Uffe Kock   +5 more
openaire   +5 more sources

Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

, 2019
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Choe, Hyeokjun, Kim, Seijoon, Park, Seongsik, Yoon, Sungroh   +3 more
core   +1 more source

Deep oscillatory neural network

Scientific Reports
We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscillatory activity.
Rohan, Nurani Rajagopal, C, Vigneswaran, Ghosh, Sayan, Rajendran, Kishore, A, Gaurav, Chakravarthy, V Srinivasa   +5 more
openaire   +2 more sources

Deep Neural Networks

, 2017
Deep Neural Networks (DNNs) are universal function approximators providing state-of- the-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now commonly tackled via DNNs.
Balestriero, Randall, Baraniuk, Richard
openaire   +2 more sources

Error Bounds for Approximations Using Multichannel Deep Convolutional Neural Networks with Downsampling

Journal of Applied Mathematics, 2023
Deep learning with specific network topologies has been successfully applied in many fields. However, what is primarily called into question by people is its lack of theoretical foundation investigations, especially for structured neural networks.
Xinling Liu, Jingyao Hou
doaj   +1 more source

Deep Neural Networks and PIDE Discretizations

SIAM Journal on Mathematics of Data Science, 2022
In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN). As an alternative to increasing the network's depth or width to improve performance, we propose integral-based spatially nonlocal operators which are related to global weighted Laplacian, fractional Laplacian and ...
Bastian Bohn, Michael Griebel, Dinesh Kannan   +2 more
openaire   +3 more sources

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Journal of Education, Health and Sport, 2021
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka, Tomasz Włodarczyk, Ryszard Szczerba, Anna Chomiak, Oskar Komisarek   +4 more
doaj   +1 more source