Neural net methods

Automation, the use of robots in industry, has not progressed with the speed that many had hoped it would. The forecasts of twenty years ago are looking fairly silly today: the fact that they were produced largely by journalists for the benefit of boardrooms of accountants and MBA's may have something to do with this, but the question of why so little has been accomplished remains.

561p toad_prince9x 30-09-2011 67 25   Download

This lecture introduces you to the fascinating subject of classification and regression with artificial neural networks. In particular, it introduces multi-layer perceptrons (MLPs); teaches you how to combine probability with neural networks so that the nets can be applied to regression, binary classification and multivariate classification; discusses the modular approach to backpropagation and neural network construction in Torch, which was introduced in the previous lecture.

20p allbymyself_08 22-02-2016 29 6   Download

This lecture introduces you sequence models. The goal is for you to learn about: Recurrent neural networks, the vanishing and exploding gradients problem, long-short term memory (LSTM) networks, applications of LSTM networks.

24p allbymyself_08 22-02-2016 48 4   Download

In this paper, we present a model-based learning for brain tumour segmentation from multimodal MRI protocols. The model uses U-Net-based fully convolutional networks to extract features from a multimodal MRI training dataset and then applies them to Extremely randomized trees (ExtraTrees) classifier for segmenting the abnormal tissues associated with brain tumour. The morphological filters are then utilized to remove the misclassified labels. Our method was evaluated on the Brain Tumour Segmentation Challenge 2013 (BRATS 2013) dataset, achieving the Dice metric of 0.85, 0.81 and 0.

7p caygaocaolon1 13-11-2019 3 0   Download

Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images.

19p viwyoming2711 16-12-2020 2 0   Download

This paper presents a method for inducing the parts of speech of a language and partof-speech labels for individual words from a large text corpus. Vector representations for the part-of-speech of a word are formed from entries of its near lexical neighbors. A dimensionality reduction creates a space representing the syntactic categories of unambiguous words. A neural net trained on these spatial representations classifies individual contexts of occurrence of ambiguous words. The method classifies both ambiguous and unambiguous words correctly with high accuracy. ...

8p bunmoc_1 20-04-2013 34 1   Download