scholarly journals The Neural Network Zoo

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 9 ◽  
Author(s):  
Stefan Leijnen ◽  
Fjodor van Veen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Information Processing ◽  
Network Architectures ◽  
Learning Models ◽  
The Neural Network ◽  
Neural Information ◽  
Neural Information Processing ◽  
Practical Tool ◽  
Neural Network Architectures

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.

scholarly journals The Neural Network Zoo

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 9
Author(s):  
Stefan Leijnen ◽  
Fjodor van Veen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Information Processing ◽  
Network Architectures ◽  
Learning Models ◽  
The Neural Network ◽  
Neural Information ◽  
Neural Information Processing ◽  
Practical Tool ◽  
Neural Network Architectures

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.

scholarly journals Obtaining Deep Learning Models for Automatic Classification of Leukocytes

2020 ◽  
pp. 1-32
Author(s):  
Pedro João Rodrigues ◽  
Getúlio Peixoto Igrejas ◽  
Romeu Ferreira Beato
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Network Architectures ◽  
Learning Models ◽  
Training Time ◽  
The Neural Network ◽  
Regularization Techniques ◽  
Microscopy Images

In this work, the authors classify leukocyte images using the neural network architectures that won the annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and the same networks trained from scratch in order to select the ones that achieve the best performance for the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The analysis of the results takes into account the amount of training required, the regularization techniques used, the training time, and the accuracy in image classification. The best classification results, on the order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.

Optimizing Deep Learning Methods in Neural Network Architectures

2021 ◽  
Vol 14 (2) ◽  
pp. 93
Author(s):  
Kristina Gorshkova ◽  
Victoria Zueva ◽  
Maria Kuznetsova ◽  
Larisa Tugashova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architectures ◽  
Learning Methods ◽  
Neural Network Architectures

scholarly journals Reply to comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”

2019 ◽  
Vol 26 (6) ◽  
pp. 580-581
Author(s):  
Anne Cocos ◽  
Alexander G Fiks ◽  
Aaron J Masino
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Peer Review ◽  
Adverse Drug Reactions ◽  
Recurrent Neural Network ◽  
Network Architectures ◽  
Drug Reactions ◽  
Detailed Review ◽  
Neural Network Architectures

Abstract We appreciate the detailed review provided by Magge et al1 of our article, “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.” 2 In their letter, they present a subjective criticism that rests on concerns about our dataset composition and potential misinterpretation of comparisons to existing methods. Our article underwent two rounds of extensive peer review and has been cited 28 times1 in the nearly 2 years since it was published online (February 2017). Neither the reviewers nor the citing authors raised similar concerns. There are, however, portions of the commentary that highlight areas of our work that would benefit from further clarification.

Sign Board Recognition Based on Convolutional Neural Network Using Yolo-3

2020 ◽  
Vol 17 (8) ◽  
pp. 3478-3483
Author(s):  
V. Sravan Chowdary ◽  
G. Penchala Sai Teja ◽  
D. Mounesh ◽  
G. Manideep ◽  
C. T. Manimegalai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Road Accidents ◽  
Learning Models ◽  
The Neural Network

Road injuries are a big drawback in society for a few time currently. Ignoring sign boards while moving on roads has significantly become a major cause for road accidents. Thus we came up with an approach to face this issue by detecting the sign board and recognition of sign board. At this moment there are several deep learning models for object detection using totally different algorithms like RCNN, faster RCNN, SPP-net, etc. We prefer to use Yolo-3, which improves the speed and precision of object detection. This algorithm will increase the accuracy by utilizing residual units, skip connections and up-sampling. This algorithm uses a framework named Dark-net. This framework is intended specifically to create the neural network for training the Yolo algorithm. To thoroughly detect the sign board, we used this algorithm.

Parallel Distributed Approaches to Combinatorial Optimization: Benchmark Studies on Traveling Salesman Problem

1990 ◽  
Vol 2 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Carsten Peterson
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Traveling Salesman ◽  
Neural Information ◽  
Neural Information Processing

We present and summarize the results from 50-, 100-, and 200-city TSP benchmarks presented at the 1989 Neural Information Processing Systems (NIPS) postconference workshop using neural network, elastic net, genetic algorithm, and simulated annealing approaches. These results are also compared with a state-of-the-art hybrid approach consisting of greedy solutions, exhaustive search, and simulated annealing.

scholarly journals A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

2018 ◽  
Author(s):  
Jessica Barbosa Diniz ◽  
Filipe R. Cordeiro ◽  
Pericles B. C. Miranda ◽  
Laura A. Tomaz Da Silva
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Genetic Programming ◽  
Convolutional Neural Network ◽  
Research Area ◽  
Classification Performance ◽  
Programming Approach ◽  
Network Architectures ◽  
Neural Network Architectures ◽  
Grammar Based Genetic Programming

Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.

scholarly journals PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations

2020 ◽  
Vol 13 (7) ◽  
pp. 3373-3382 ◽  
Author(s):  
Olivier Pannekoucke ◽  
Ronan Fablet
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architectures ◽  
Computationally Efficient ◽  
Partial Differential ◽  
Compact Representations ◽  
Network Generator ◽  
Open Issue ◽  
Neural Network Architectures ◽  
Pde Solvers

Abstract. Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue. In the spirit of physics-informed neural networks (NNs), the PDE-NetGen package provides new means to automatically translate physical equations, given as partial differential equations (PDEs), into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The latter exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally efficient yet compact representations to address a variety of issues, including, among others, adjoint derivation, model calibration, forecasting and data assimilation as well as uncertainty quantification. As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.

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