Academic Articles & Research Papers

Applications of Neural Networks in Electromagnetics

From the Book: Since the early 1990s, a plethora of electromagnetic problems have been tackled using neural networks, some more successfully than others. Because neural networks and electromagnetics are two different scientific fields, not...

Artificial Neural Networks

Neural Networks for Identification, Prediction and Control

Recurrent Neural Networks

From the Publisher: With applications ranging from motion detection to financial forecasting, recurrent neural networks (RNNs) have emerged as an interesting and important part of neural network research. Recurrent Neural Networks: Design ...

The basic ideas in neural networks

article Free AccessThe basic ideas in neural networks Authors: David E. Rumelhart Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile ,...

Bankruptcy prediction using neural networks

Fundamentals of Neural Networks

An Introduction to Convolutional Neural Networks

Abstract: The field of machine learning has taken a dramatic twist in re- cent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models can far exceed the per- performance of previous for...

An introduction to neural networks

Introduction to Artificial Neural Network

Pulsed Neural Networks

Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have...

On Interpretability of Artificial Neural Networks: A Survey

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become ...

Neural Networks for Control

cuDNN: Efficient Primitives for Deep Learning

We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels mu...

Applied Deep Learning

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks, in this compr...

Deep Learning for Instance Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, par...

Federated Machine Learning

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to t...

Proceedings of the 22nd international conference on Machine learning

This volume, which is also available online from http://www.machinelearning.org, contains the papers accepted for presentation at ICML-2005, the 22nd lnternational Conference on Machine Learning, which was held at the University of Bonn in ...

Artificial Intelligence

Artificial Intelligence (AI) refers to a set of technologies through which "intelligent machines are gaining the ability to learn, improve and make calculated decisions in ways that enable them to perform tasks previously thought to rely so...

Geoscientists’ Perspectives on Cyberinfrastructure Needs: A Collection of User Scenarios

Cyberinfrastructure (CI) is a standard tool in the geosciences, but the creation of successful CI remains difficult, and expensive projects can have significant consequences for scientific communities if they do not result in success. In th...