Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer
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Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...
We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a ...
This report presents one month trainee work on development of French Automatic Speech Recognition ASR system using a french part of multilingual database GlobalPhone_FR. The purpose of this report is to explain and give results of the training and testing ...
In this paper, we propose a novel parts-based binary-valued feature for ASR. This feature is extracted using boosted ensembles of simple threshold-based classifiers. Each such classifier looks at a specific pair of time-frequency bins located on the spectr ...
How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes’ behaviors (e.g., the time ...
We analyze a simple hierarchical architecture consisting of two multilayer perceptron (MLP) classifiers in tandem to estimate the phonetic class conditional probabilities. In this hierarchical setup, the first MLP classifier is trained using standard acous ...
In this paper, we propose a taxonomy of handwriting errors exhibited by children as a way to build adequate strategies for integration with a co-writing peer. The exploration includes the collection of letters written by children in an initial study, which ...
A novel parts-based binary-valued feature termed Boosted Binary Feature (BBF) was recently proposed for ASR. Such features look at specific pairs of time-frequency bins in the spectro-temporal plane. The most discriminative of these features are selected b ...
We investigate a multilayer perceptron (MLP) based hierarchical approach for task adaptation in automatic speech recognition. The system consists of two MLP classifiers in tandem. A well-trained MLP available off-the-shelf is used at the first stage of the ...
In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in ...