How can quantum field theories be best formulated on a lattice to optimally simulate them on a computer? The answer comes ...
Deep neural networks (DNNs) have become a cornerstone of modern AI technology, driving a thriving field of research in ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...
This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.
Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...
CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...
Abstract: In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy ...
This project presents a complete workflow for cone detection in Formula Student Driverless scenarios using deep learning. It demonstrates how to use MATLAB® and Simulink® for data preparation and ...
A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...
Abstract: Convolutional Neural Networks (CNNs) have shown remarkable success across numerous tasks such as image classification, yet the theoretical understanding of their convergence remains ...
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