Results 231 to 240 of about 218,844 (266)

Deep Morphological Neural Networks

International Journal of Pattern Recognition and Artificial Intelligence, 2022
Mathematical morphology intends to extract object features such as geometric and topological structures in digital images. Given a set of target images and original images, it is cumbersome and time-consuming to determine the suitable morphological operations and structuring elements. In this paper, we propose deep morphological neural networks, which
Yucong Shen, Frank Y. Shih, Xin Zhong 0001, I-Cheng Chang   +3 more
openaire   +1 more source

Tracking with deep neural networks

2013 47th Annual Conference on Information Sciences and Systems (CISS), 2013
We present deep neural network models applied to tracking objects of interest. Deep neural networks trained for general-purpose use are introduced to conduct long-term tracking, which requires scale-invariant feature extraction even when the object dramatically changes shape as it moves in the scene.
Jonghoon Jin, Aysegul Dundar, Jordan Bates, Clément Farabet, Eugenio Culurciello   +4 more
openaire   +1 more source

Deep Neural Networks for QSAR

2021
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures.
openaire   +2 more sources

Fissionable Deep Neural Network

2016
Model combination nearly always improves the performance of machine learning methods. Averaging the predictions of multi-model further decreases the error rate. In order to obtain multi high quality models more quickly, this article proposes a novel deep network architecture called “Fissionable Deep Neural Network”, abbreviated as FDNN. Instead of just
Dongxu Tan, Junmin Wu, Huanxin Zheng, Yan Yin, Yaxin Liu   +4 more
openaire   +1 more source

Deep Neural Networks-II

2019
We will implement a multi-layered neural network with different hyperparameters Hidden layer activations Hidden layer nodes Output layer activation Learning rate Mini-batch size Initialization Value of \(\beta \) Values of \(\beta _1\) Value of \(\beta _2\) Value of \(\epsilon \) Value of keep_prob
openaire   +1 more source

On the Singularity in Deep Neural Networks

2016
In this paper, we analyze a deep neural network model from the viewpoint of singularities. First, we show that there exist a large number of critical points introduced by a hierarchical structure in the deep neural network as straight lines. Next, we derive sufficient conditions for the deep neural network having no critical points introduced by a ...
openaire   +1 more source

Deep neural network

2022
REDA SHERIEF, TANN HOKCHHAY, HASHEMI SOHEIL, BAHAR R IRIS   +3 more
openaire   +2 more sources

Convergence of deep convolutional neural networks

Neural Networks, 2022
Haizhang Zhang
exaly  

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Sensors, 2021
Muhammad Fazal Ijaz, Deepak Sinwar, Geeta Rani   +2 more
exaly  

Theory of deep convolutional neural networks: Downsampling

Neural Networks, 2020
Ding-Xuan Zhou
exaly