Academic Articles & Research Papers

Quantifying Uncertainties in Natural Language Processing Tasks

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

Neural Network Methods for Natural Language Processing

Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...

Using natural language processing to understand people and culture.

Language can provide important insights into people, and culture more generally. Further, the digitization of information has made more and more textual data available. But by itself, all that data are just that: data. Realizing its potenti...

Natural Language Processing

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...

Deep Neural Networks for Natural Language Processing

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...

Part-of-speech tagging with neural networks

Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method based on neural networks (Net- Tagger) is presented and its performance is com...

Spectrally-normalized margin bounds for neural networks

This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrice...

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...

Neural networks in control systems

Some of the problems that arise in the control of nonlinear systems in the presence of uncertainty are considered. Multilayer neural networks and radial basis function networks are used in the design of identifiers and controllers, and grad...

Weight Uncertainty in Neural Networks

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression ...

Autoassociative neural networks

Neural Networks in System Identification

Statistical Mechanics of Neural Networks

A neural network is a large, highly interconnected assembly of simple elements. The elements, called neurons, are usually two-state devices that switch from one state to the other when their input exceeds a specific threshold value. In this...

Siamese Neural Networks: An Overview

Feed-forward neural networks

One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the numbe...

Circuit Complexity and Neural Networks

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how wel...

Neural Networks for Statistical Modeling

A neural network approach to ordinal regression

Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...

Neural Networks in Structural Engineering

In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...