Academic Articles & Research Papers

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. R...

A literature survey of active machine learning in the context of natural language processing

Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain ...

A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

LSTM neural networks for language modeling

Neural networks have become increasingly popular for the task of language modeling.Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks ca...

Forecast combining with neural networks

This paper investigates the use of Artificial Neural Networks (ANNs) to combine time series forecasts of stock market volatility from the USA, Canada, Japan and the UK. We demonstrate that combining with nonlinear ANNs generally produces fo...

A neural-network-based detection of epilepsy

Diagnosis of epilepsy is primarily based on scalp-recorded electroencephalograms (EEG). Unfortunately the long-term recordings obtained from 'ambulatory recording systems' contain EEG data of up to one week duration, which has introduced ne...

On the Spectral Bias of Neural Networks

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. ...

Memristor devices for neural networks

Abstract Neural network technologies have taken center stage owing to their powerful computing capability for supporting deep learning in artificial intelligence. However, conventional synaptic devices such as SRAM and DRAM are not satisfac...

Hyperbolic Neural Networks

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...

Neural networks in process fault diagnosis

Fault detection and diagnosis is an important problem in process automation. Both model-based methods and expert systems have been suggested to solve the problem, along with the pattern recognition approach. A number of possible neural netw...

Artificial neural networks for speech and vision

Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peri...

SNNS (Stuttgart Neural Network Simulator)

Heterogeneous Graph Neural Network

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...

State-of-the-art in artificial neural network applications: A survey

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of current and emerging trends in ANN applications research and ar...

Emerging Principles Governing The Operation Of Neural Networks

ical, and computational techniques have provided the tools to begin uncovering concepts underlying neural network function. This review is a summary of insights into the organization and operation of neural circuits acquired through applica...

A neural network approach to cloud classification

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery ...

Neural network design for engineering applications

Neural network computing

At present, computers cannot think, make independent decisions, recognize visual and audible patterns, reach reliable conclusions without complete information or learn from their mistakes. The question this book and research into artificial...

A NEURAL NETWORK PRIMER

Neural networks are composed of basic units somewhat analogous to neurons. These units are linked to each other by connections whose strength is modifiable as a result of a learning process or algorithm. Each of these units integrates indep...

A view of Artificial Neural Network

In this paper, An Artificial Neural Network or ANN, its various characteristics and business applications. In this paper also show that “what are neural networks” and “Why they are so important in today's Artificial intelligence?” Because v...