Results 251 to 260 of about 827,210 (276)

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 Learning with Random Neural Networks

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This paper develops multi-layer classifiers and auto-encoders based on the Random Neural Network. Our motivation is to build robust classifiers that can be used in systems applications such as Cloud management for the accurate detection of states that can lead to failures.
Erol Gelenbe, Yongha Yin
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

Automated Design of Deep Neural Networks

ACM Computing Surveys, 2022
El-Ghazali Talbi
exaly  

Adversarial Perturbation Defense on Deep Neural Networks

ACM Computing Surveys, 2022
Xingwei Zhang, Xiaolong Zheng, Wenji Mao   +2 more
exaly  

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Computers in Biology and Medicine, 2022
Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee   +2 more
exaly  

Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality

Heliyon, 2021
Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Charles L Bormann   +2 more
exaly