A Study of Random Neural Network Performance for Supervised Learning Tasks in CUDA

The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to other artificial neural networks. This paper considers non-negative least squares supervised learning in this context, and develops an approach that achieves fast execution and excellent learning capacity. This speedup is a result of significant enhancements in the solution of the non-negative least-squares problem which regard (a) the development of analytical expressions for the evaluation of the gradient and objective functions and (b) a novel limited-memory quasi-Newton solution algorithm. Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach, achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms.

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A more powerful random neural network model in supervised learning applications

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) ◽

10.1109/socpar.2013.7054127 ◽

2013 ◽

Cited By ~ 2

Author(s):

Sebastian Basterrech ◽

Gerardo Rubino

Keyword(s):

Neural Network ◽

Supervised Learning ◽

Network Model ◽

Neural Network Model ◽

Random Neural Network

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Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽

10.3390/e23040403 ◽

2021 ◽

Vol 23 (4) ◽

pp. 403

Author(s):

Xun Zhang ◽

Lanyan Yang ◽

Bin Zhang ◽

Ying Liu ◽

Dong Jiang ◽

...

Keyword(s):

Neural Network ◽

Supervised Learning ◽

Structured Data ◽

Similarity Matrix ◽

Scale Feature ◽

Multi Scale ◽

Adjacent Node ◽

Feature Similarity ◽

The Mean ◽

Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

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LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211019544 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110195

Author(s):

Sorin Grigorescu ◽

Cosmin Ginerica ◽

Mihai Zaha ◽

Gigel Macesanu ◽

Bogdan Trasnea

Keyword(s):

Neural Network ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Autonomous Vehicles ◽

Process Model ◽

Network Performance ◽

Vision System ◽

Nonlinear Model Predictive Control ◽

Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

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TPOT-NN: augmenting tree-based automated machine learning with neural network estimators

Genetic Programming and Evolvable Machines ◽

10.1007/s10710-021-09401-z ◽

2021 ◽

Author(s):

Joseph D. Romano ◽

Trang T. Le ◽

Weixuan Fu ◽

Jason H. Moore

Keyword(s):

Neural Network ◽

Machine Learning ◽

Binary Classification ◽

Inductive Learning ◽

Future Directions ◽

High Performing ◽

Learning Tasks ◽

Benchmark Datasets ◽

Automated Machine Learning ◽

Standard Tree

AbstractAutomated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists on when to use one versus the other. Furthermore, relatively few tools exist that allow the integration of both AutoML and ANNs in the same analysis to yield results combining both of their strengths. Here, we present TPOT-NN—a new extension to the tree-based AutoML software TPOT—and use it to explore the behavior of automated machine learning augmented with neural network estimators (AutoML+NN), particularly when compared to non-NN AutoML in the context of simple binary classification on a number of public benchmark datasets. Our observations suggest that TPOT-NN is an effective tool that achieves greater classification accuracy than standard tree-based AutoML on some datasets, with no loss in accuracy on others. We also provide preliminary guidelines for performing AutoML+NN analyses, and recommend possible future directions for AutoML+NN methods research, especially in the context of TPOT.

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