Separable Attention Capsule Network for Signal Classification

Modified Neocognitron Network for Medical Signal Classification

A modified neocognitron neural network suitable for medical signal classification is presented. The network's functionality is demonstrated on an application involving the classification of breathing signals measured on patients recovering from surgery. The performance of the system was found to be equivalent, and in some cases, better than a standard technique used for comparison. The main adv...

Separable Linear Discriminant Classification

Linear discriminant analysis is a popular technique in computer vision, machine learning and data mining. It has been successfully applied to various problems, and there are numerous variations of the original approach. This paper introduces the idea of separable LDA. Towards the problem of binary classification for visual object recognition, we derive an algorithm for training separable discri...

Attention-based LSTM Network for Cross-Lingual Sentiment Classification

Most of the state-of-the-art sentiment classification methods are based on supervised learning algorithms which require large amounts of manually labeled data. However, the labeled resources are usually imbalanced in different languages. Cross-lingual sentiment classification tackles the problem by adapting the sentiment resources in a resource-rich language to resource-poor languages. In this ...

Neural network adaptive wavelets for signal representation and classification

Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification

This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract translation invariant representations from an input signal. The computationally efficient Dual-Tree wavelets decompose the input signal into densely spaced representations over scales. Translation invariance is introduced in the representations by applying a non-linearity over a region followed b...