scholarly journals Neural Layer Bypassing Network

2021 ◽  
Author(s):  
Amogh Palasamudram
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Network Architecture ◽  
Classification Models ◽  
Learning Models ◽  
Neural Network Architecture ◽  
Overall Performance ◽  
The Impact

<p>This research aims to introduce and evaluate a new neural network architecture to improve the speed and effectiveness of forward propagation in neural networks: the Neural Layer Bypassing Network (NLBN). The theory and workings of this architecture have been explained in this research paper, along with comparisons to other methods of increasing the efficacy of deep learning models. This research also includes code examples with 3 image classification models trained on different datasets and analyses the impact of the NLBN architecture on forward propagation. It was found that this architecture increases the speed of forward propagation and tends to slightly decrease the accuracy of the model. However, it takes longer to train and takes more memory. All in all, this architecture is a potential foundation for using deep learning to teach deep learning models to be more efficient. This includes skipping and re-propagating through layers to improve the overall performance of a model.</p><div><br></div>

scholarly journals A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

2021 ◽  
Vol 10 (12) ◽  
pp. 7-12
Author(s):  
Karthika Gidijala ◽  
◽  
Mansa Devi Pappu ◽  
Manasa Vavilapalli ◽  
Mahesh Kothuru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Proposed Model ◽  
Histopathological Images

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.

scholarly journals AMBIENT: Accelerated Convolutional Neural Network Architecture Search for Regulatory Genomics

2021 ◽  
Author(s):  
Zijun Zhang ◽  
Evan M. Cofer ◽  
Olga G. Troyanskaya
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Environmental Issue ◽  
Biological Sequences ◽  
Neural Network Architecture ◽  
Computing Power ◽  
Neural Architecture

Convolutional neural networks (CNN) have become a standard approach for modeling genomic sequences. CNNs can be effectively built by Neural Architecture Search (NAS) by trading computing power for accurate neural architectures. Yet, the consumption of immense computing power is a major practical, financial, and environmental issue for deep learning. Here, we present a novel NAS framework, AMBIENT, that generates highly accurate CNN architectures for biological sequences of diverse functions, while substantially reducing the computing cost of conventional NAS.

scholarly journals Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6109
Author(s):  
Nkosikhona Dlamini ◽  
Terence L. van Zyl
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Network Architecture ◽  
Loss Functions ◽  
Similarity Learning ◽  
Neural Network Architecture ◽  
Shot Classification ◽  
Public Datasets ◽  
The Impact

Similarity learning using deep convolutional neural networks has been applied extensively in solving computer vision problems. This attraction is supported by its success in one-shot and zero-shot classification applications. The advances in similarity learning are essential for smaller datasets or datasets in which few class labels exist per class such as wildlife re-identification. Improving the performance of similarity learning models comes with developing new sampling techniques and designing loss functions better suited to training similarity in neural networks. However, the impact of these advances is tested on larger datasets, with limited attention given to smaller imbalanced datasets such as those found in unique wildlife re-identification. To this end, we test the advances in loss functions for similarity learning on several animal re-identification tasks. We add two new public datasets, Nyala and Lions, to the challenge of animal re-identification. Our results are state of the art on all public datasets tested except Pandas. The achieved Top-1 Recall is 94.8% on the Zebra dataset, 72.3% on the Nyala dataset, 79.7% on the Chimps dataset and, on the Tiger dataset, it is 88.9%. For the Lion dataset, we set a new benchmark at 94.8%. We find that the best performing loss function across all datasets is generally the triplet loss; however, there is only a marginal improvement compared to the performance achieved by Proxy-NCA models. We demonstrate that no single neural network architecture combined with a loss function is best suited for all datasets, although VGG-11 may be the most robust first choice. Our results highlight the need for broader experimentation and exploration of loss functions and neural network architecture for the more challenging task, over classical benchmarks, of wildlife re-identification.

scholarly journals Neural Layer Bypassing Network

2022 ◽  
Author(s):  
Amogh Palasamudram
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Classification Models ◽  
Neural Network Architecture ◽  
Network Layers ◽  
Time Period ◽  
Computationally Intensive ◽  
Fully Connected ◽  
The Given

<p>This research introduces and evaluates the Neural Layer Bypassing Network (NLBN), a new neural network architecture to improve the speed and effectiveness of forward propagation in deep learning. This architecture utilizes 1 additional (fully connected) neural network layer after every layer in the main network. This new layer determines whether finishing the rest of the forward propagation is required to predict the output of the given input. To test the effectiveness of the NLBN, I programmed coding examples for this architecture with 3 different image classification models trained on 3 different datasets: MNIST Handwritten Digits Dataset, Horses or Humans Dataset, and Colorectal Histology Dataset. After training 1 standard convolutional neural network (CNN) and 1 NLBN per dataset (both of equivalent architectures), I performed 5 trials per dataset to analyze the performance of these two architectures. For the NLBN, I also collected data regarding the accuracy, time period, and speed of the network with respect to the percentage of the model the inputs are passed through. It was found that this architecture increases the speed of forward propagation by 6% - 25% while the accuracy tended to decrease by 0% - 4%; the results vary based on the dataset and structure of the model, but the increase in speed was normally at least twice the decrease in accuracy. In addition to the NLBN’s performance during predictions, it takes roughly 40% longer to train and requires more memory due to its complexity. However, the architecture can be made more efficient if integrated into TensorFlow libraries. Overall, by being able to autonomously skip neural network layers, this architecture can potentially be a foundation for neural networks to teach themselves to become more efficient for applications that require fast, accurate, and less computationally intensive predictions.<br></p>

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

2016 ◽  
Vol 807 ◽  
pp. 155-166 ◽  
Author(s):  
Julia Ling ◽  
Andrew Kurzawski ◽  
Jeremy Templeton
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reynolds Stress ◽  
Network Architecture ◽  
Eddy Viscosity ◽  
Deep Neural Networks ◽  
Test Cases ◽  
Neural Network Architecture ◽  
Stress Anisotropy ◽  
Anisotropy Tensor

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

Sign in / Sign up

Export Citation Format

Share Document