When neurons form a multiplex network of 20 HR neurons arranged in two equal-size modules in a bottleneck configuration, communication between pairs of neurons in the two modules is mostly efficient when using either the spike-timings or the maximum points of their phases codes. 2. Materials and methods. 2.1.

This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as ...

There are two ways to approach an overfit model: Reduce overfitting by training the network on more examples. Reduce overfitting by changing the complexity of the network. A benefit of very deep neural networks is that their performance continues to improve as they are fed larger and larger datasets.

Specifically, we show that well-performing and efficient models can be realized by virtue of Spiking Neural Networks (SNNs), reaching competitive levels of detection performance when compared to their non-spiking counterparts at dramatic energy consumption savings (up to 85%) and a slightly improved robustness against …

Average Model Performance. We can counter the variance in the solution found by a specific neural network by summarizing the performance of the approach over multiple runs. This involves fitting the same algorithm on the same dataset multiple times but allowing the randomness used in the learning algorithm to vary each time the …

This is where the meat is. You can often unearth one or two well-performing algorithms quickly from spot-checking. Getting the most from those algorithms can take, days, weeks or months. Here are some ideas on tuning your neural network algorithms in order to get more out of … See more

It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). ... To improve on the performance of previous networks, …

Neural network target values, specified as a matrix or cell array of numeric values. Network target values define the desired outputs, and can be specified as an N-by-Q matrix of Q N-element vectors, or an M-by-TS cell array where each element is an Ni-by-Q matrix. In each of these cases, N or Ni indicates a vector length, Q the number of samples, M …

1. Latency. Latency is the amount of time it takes for a neural network to produce a prediction for a single input sample. To measure the latency of a neural network in PyTorch, we can use the time module to track the time taken to perform a forward pass through the network. we will use Pustil library from python to show you how could we …

One of the solutions is to repeat the prediction several times and calculate statistics of those results. Code for 30 repetitions / average statistics of the 30 repetitions. Thus, I repeated, and ...

To achieve better performance of a diffractive deep neural network, increasing its spatial complexity (neurons and layers) is commonly used. Subject to physical laws of optical diffraction, a deeper diffractive neural network (DNN) would be more difficult to implement, and the development of DNN is limited. In this work, we found controlling the Fresnel …

Training neural networks can be a time-consuming process, especially when dealing with complex models and large datasets. As deep learning continues to advance, optimizing the performance of neural…

Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the …

The development of training strategies for neural networks is one of three major reasons for the bloom of DNNs 2 In general, an effective training strategy includes two factors: (1) an appropriate objective (also called the cost function) and (2) the optimization algorithm of the objective. Below, we introduce some representative works …

For example, Çelik and Karatepe [10] examined the performance of neural networks use in evaluating and forecasting of banking crises; Wang and Chien [37] presented a forecasting model that predicts innovation performance using technical informational resources and clear innovation objectives by back-propagation neural …

We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of ...

Neural Networks (CNNs), pyramid networks or attention mechanisms, to mention a few. Such proposals have elevated object detection to unprecedented levels of performance, as evinced by comprehensive surveys on this topic [4]. Unfortunately, despite the reported effectiveness of DL techniques for this modeling task, two major problems still

An artificial neural network (ANN) or a simple traditional neural network aims to solve trivial tasks with a straightforward network outline. An artificial neural network is loosely inspired from biological neural networks. It is a collection of layers to perform a specific task. Each layer consists of a collection of nodes to operate together.

Applying Initialization. Initialization is one of the first techniques used to fasten the training time of Neuron Network (as well as to improve performance). In Artificial Neural Network (ANN), there are numerous connections between different neurons. One neuron in the current layer connects to several neurons in the next layer and is attached ...

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the …

How to Improve Neural Network Stability and Modeling Performance With Data Scaling. Photo by Javier Sanchez Portero, …

Learning rate. The learning rate defines how quickly a network updates its parameters. Low learning rate slows down the learning process but converges smoothly.Larger learning rate speeds up the learning but may not converge.. Usually a decaying Learning rate is preferred.. Momentum. Momentum helps to know the direction …

Deep learning is a subset of machine learning that uses multi-layered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) in our lives today. By strict definition, a deep neural network, or DNN, is a neural ...

This paper considers the use of neural networks—namely self-organizing maps (SOMs)—to analyze and cluster firms' financial performance. Applying SOMs to financial statement data is a consolidated practice; however, in this paper SOMs are used to overcome several limitations encountered in previous works on financial reporting …

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This Leveraging …

In the emerging mainstream methodology of material design, whether it entails the modeling neural network for inverse mapping of desired performance (Liu et al., 2018) or utilizing Gaussian processes for forward optimization within uncharted material spaces (Iyer et al., 2019), one crucial step is the effective evaluation of material properties ...

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images. by. Bo Zang. 1, Linlin Ding. 1, Zhenpeng Feng. 1,*, Mingzhe Zhu. 1, Tao Lei. 2, Mengdao Xing. …

Saddle point — simultaneously a local minimum and a local maximum. An example function that is often used for testing the performance of optimization algorithms on saddle points is the …

Abstract. Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space and network parameters. It can consistently increase the agent's exploration ability and ...

perf = crossentropy(net,targets,outputs,perfWeights) calculates a network performance given targets and outputs, with optional performance weights and …

The easiest way to think about artificial intelligence, machine learning, deep learning and neural networks is to think of them as a series of AI systems from largest to smallest, each encompassing the next. Artificial intelligence is the overarching system. Machine learning is a subset of AI. Deep learning is a subfield of machine learning ...

Deep neural networks have garnered extremely high traction due to their high efficiency in achieving numerous varieties of deep learning projects. Explore the differences between …

The more training examples used in the estimate, the more accurate this estimate will be and the more likely that the weights of the network will be adjusted in a way that will improve the performance of the model.

%0 Conference Proceedings %T Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction %A Lin, Chen %A Miller, Timothy %A Dligach, Dmitriy %A Amiri, Hadi %A Bethard, Steven %A Savova, Guergana %Y Lavelli, Alberto %Y Minard, Anne-Lyse %Y Rinaldi, Fabio %S …