deep neural network 2018






Relation classification via convolutional deep neural network
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The state-of-the-art methods used for relation classification are primarily based on statistical machine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural

Deep Neural Network Embeddings for Text-Independent Speaker Verification.
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This paper investigates replacing i-vectors for text-independent speaker verification with embeddings extracted from a feedforward deep neural network . Long-term speaker characteristics are captured in the network by a temporal pooling layer that aggregates over

Restructuring of deep neural network acoustic models with singular value decomposition.
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Recently proposed deep neural network (DNN) obtains significant accuracy improvements in many large vocabulary continuous speech recognition (LVCSR) tasks. However, DNN requires much more parameters than traditional systems, which brings huge cost during

Deep neural network approach for the dialog state tracking challenge
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While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking

Privacy-preserving classification on deep neural network .
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Neural Networks (NN) are today increasingly used in Machine Learning where they have become deeper and deeper to accurately model or classify high-level abstractions of data. Their development however also gives rise to important data privacy risks. This observation

Word alignment modeling with context dependent deep neural network
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In this paper, we explore a novel bilingual word alignment approach based on DNN ( Deep Neural Network ), which has been proven to be very effective in various machine learning tasks (Collobert et al.). We describe in detail how we adapt and extend the CD

Tweet sarcasm detection using deep neural network
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Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design

Stimulated deep neural network for speech recognition
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Deep neural networks (DNNs) and deep learning approaches yield state-of-the-art performance in a range of tasks, including speech recognition. However, the parameters of the network are hard to analyze, making network regularization and robust adaptation

A deep neural network compression pipeline: Pruning, quantization, huffman encoding
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Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, We introduce a three stage pipeline: pruning, quantization and Huffman encoding

Likability Classification--A not so Deep Neural Network Approach
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This papers presents results on the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) on the Likability Sub-Challenge of the Interspeech 2012 Speaker Trait Challenge . RBMs are a particular form of log-linear Markov Random Fields

End-to-end deep neural network for automatic speech recognition
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We investigate the efficacy of deep neural networks on speech recognition. Specifically, we implement an end-to-end deep learning system that utilizes mel-filter bank features to directly output to spoken phonemes without the need of a traditional Hidden Markov Model

Deep neural network based feature representation for weather forecasting
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This paper concentrated on a new application of Deep Neural Network (DNN) approach. The DNN, also widely known as Deep Learning (DL), has been the most popular topic in research community recently. Through the DNN, the original data set can be represented in

SNR-Based Progressive Learning of Deep Neural Network for Speech Enhancement.
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In this paper, we propose a novel progressive learning (PL) framework for deep neural network (DNN) based speech enhancement. It aims at decomposing the complicated regression problem of mapping noisy to clean speech into a series of subproblems for

A protein-protein interaction extraction approach based on deep neural network
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Protein Protein Interactions (PPIs) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Machine learning methods have been the most popular ones in PPI extraction area. However, these methods are still feature

Learning Temporal Features Using a Deep Neural Network and its Application to Music Genre Classification.
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In this paper, we describe a framework for temporal feature learning from audio with a deep neural network , and apply it to music genre classification. To this end, we revisit the conventional spectral feature learning framework, and reformulate it in the cepstral

Deep neural network based text-dependent speaker recognition: Preliminary results
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Recently there has significant research interest in using neural networks as feature extractors for text-dependent speaker verification. These types of systems have been shown to perform very well when a large amount of speaker data is available for training. In this

Deep neural network based load forecast
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W He- Comput. Model. New Technol cmnt.lv Accurate electrical load forecast has great economic and social value. In this paper, we study deep neural networks based load forecast approaches. We first analyse the critical features related to load forecast. Then we present details of deep neural networks and pre

Diagnosis of the parkinson disease by using deep neural network classifier
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Parkinson disease occurs when certain clusters of brain cells are unable to generate dopamine which is needed to regulate the number of the motor and non-motor activity of the human body. Besides, contributing to speech, visual, movement, urinary problems

Deep Neural Network Acoustic Models for ASR.
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Abdel-rahman Mohamed Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 Automatic speech recognition (ASR) is a key core technology for the information age. ASR systems have evolved from discriminating among isolated digits to

Vehicle classification using transferable deep neural network features
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We address vehicle detection on rear view vehicle images captured from a distance along multi-lane highways, and vehicle classification using transferable features from Deep Neural Network . We address the following problems that are specific to our application: how to




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