Academic Articles & Research Papers

Neural network studies

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...

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world’s most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate ...

EdgeAIGC: Model caching and resource allocation for edge artificial intelligence generated content

With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-s...