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In this paper we propose a modification of the Cognitive Architectures for Sensory Processing proposed by Chalasani and Principe. Here we keep the bottom-up data representation through generative models as before, but propose a top-down flow based on backpropagation of gradients for recognition. By treating the bottom-up procedure involved in the inference step as a recursive neural network, we show...
Recently Deep neural networks (DNN) have achieved a lot of success and become the most popular approach for speech recognition. DNN training for speech recognition is a difficult process due to its large number of parameters and speech dataset size. Using DNNs in a modeling task can be improved when pre-training is done using additional information. In this paper, we propose a new approach namely...
The aim of this paper is to evaluate the effectiveness of a class of data-driven physical models to represent both acoustic and high-speed video data of the voice production process. Voice production analysis through numerical models of the phonation process is nowday a mature research field, and reliable dynamical glottal models of different accuracy and complexity are available. Although they are...
Financial markets are very important to the economical and social organization of modern society. Due to they importance, several researchers have investigated how to predict future market movements by using both statistical and soft computing methods based on historical time series data. However, as a typical data stream, financial time series frequently present concept drift, which is a change in...
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