Results 51 to 60 of about 203,239 (310)
Convolutional Neural Networks In Convolution
CoRR, 2018 Currently, increasingly deeper neural networks have been applied to improve their accuracy. In contrast, We propose a novel wider Convolutional Neural Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks and the Network In Network(NIN), aiming for higher accuracy without input data transmutation.
openaire +2 more sources
Normalized Convolutional Neural Network
CoRR, 2020 We introduce a Normalized Convolutional Neural Layer, a novel approach to normalization in convolutional networks. Unlike conventional methods, this layer normalizes the rows of the im2col matrix during convolution, making it inherently adaptive to sliced inputs and better aligned with kernel structures. This distinctive approach differentiates it from
Dongsuk Kim +4 more
openaire +2 more sources
Dual-channel deep graph convolutional neural networks
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
doaj +1 more source
, 2016 Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla +3 more
core +1 more source
Application of shallow and deep convolutional neural networks to recognize the average flow rate of physiological fluids in a capillary [PDF]
, 2022 The aim of this work is to develop practical tools to recognize the average flow rate of physiological fluids in capillaries. This tool is represented by classification models in an artificial neural networks form.
Kornaeva, Elena +3 more
core +1 more source
An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
IEEE AccessRecent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks.
Li Hai +4 more
doaj +1 more source
Classification methods for handwritten digit recognition: A survey
Vojnotehnički Glasnik, 2023 Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit ...
Ira M. Tuba +2 more
doaj +1 more source
An Introduction to Convolutional Neural Networks
CoRR, 2015 10 pages, 5 ...
Keiron O'Shea, Ryan Nash
openaire +2 more sources
Homological Convolutional Neural Networks
CoRR, 2023 Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still pose a challenge, with classic machine learning approaches being often computationally cheaper and equally effective than increasingly complex deep learning ...
Antonio Briola +3 more
openaire +3 more sources
Artificial Neural Networks and Evolutionary Computation in Remote Sensing [PDF]
, 2021 Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images.
core +1 more source