Top PDF On Recurrent and Deep Neural Networks
On Recurrent and Deep Neural Networks
... of Deep Neural Networks (Montufar, Pascanu, Cho, and Bengio, 2014) is submitted to the Conference on Neural Information Processing Systems (NIPS) 2014 and is work done jointly ...
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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... simple Recurrent Neural Network (RNN) and our multimodal Recurrent Neural Network (m-RNN) ...image and its corresponding sentence ...start and an end sign w end to all the ...
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Quantization and Deployment of Deep Neural Networks on Microcontrollers
... learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human Activity ...
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Sequential modeling, generative recurrent neural networks, and their applications to audio
... trons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three ...
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Segmentation and Classification of Opinions with Recurrent Neural Networks
... government and other entities, who want to understand the likes, dislikes and feedback of the users and people in ...segmentation and classification of opinions in text. We propose a ...
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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
... is deep in all of these senses. See Section 3.2. Recursive Neural Networks and Convolutional Recurrent Neural Networks: When unfolding RNN into a feedforward network, the ...
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Mean-field Langevin System, Optimal Control and Deep Neural Networks
... the deep neural ...layer) neural networks using the mean-field Langevin ...the deep neural ...the deep neural network under mild ...(1.7) and its relation to ...
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Towards better understanding and improving optimization in recurrent neural networks
... Abstract Recurrent neural networks (RNN) are known for their notorious exploding and vanishing gradient problem ...parametric and semi- parametric RNNs to gain a better understanding ...
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Deep neural networks for natural language processing and its acceleration
... Recurrent neural networks (RNNs) have shown tremendous success in modeling sequen- tial data, such as natural language [119, ...dependencies and stimulating research on strategies to deal ...
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Adaptive structured noise injection for shallow and deep neural networks
... of deep neural networks (DNN) for many machine learning tasks such as image classification and object recognition (Krizhevsky et ...Tishby and Zaslavsky, 2015; Mallat, ...redundancy ...
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Entropy and mutual information in models of deep neural networks
... of deep learning models with a tractable method to compute information- theoretic ...entropies and mutual informations can be derived from heuristic statistical physics methods, under the assumption that ...
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Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks
... Results and discussion ...640 and 160 neurons ...Das and Petrov (2011), Duong et ...it) and Gouws and Søgaard (2015a) (who in addition used Wik- tionary and Wikipedia) ...Greek ...
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Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
... Kerkeni and al. [2] and modulation spectral features (MSFs) ...features and classifiers for Berlin and Spanish ...MFCC and MS has the highest accuracy rate on both Spanish emotional ...
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De-noising and de-blurring of images using deep neural networks
... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ...
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Clinical event prediction and understanding with deep neural networks
... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ...
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Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
... in deep learning framework training loops, the empirical loss of an epoch is computed as the averaged loss of each ...stopping and best epoch ...weights and use it to obtain the corresponding bound ...
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Designing Regularizers and Architectures for Recurrent Neural Networks
... 2.2.5.2 Cost functions for AGI An unconstrained search for an algorithm with low cost can return unexpected solu- tions, which may have undesirable behaviours, if the cost does not properly reflect ev- erything that is ...
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Singing voice detection with deep recurrent neural networks
... signal and provide these features as an input to a classification system such as Support Vector Machines (SVMs) [3, 5], Hidden Markov Models (HMMs) [2], Random Forests [6, 7] or Artificial Neural ...
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On Deep Multiscale Recurrent Neural Networks
... (2) Data Sparsity There is a further technical reason why much of previous re- search on machine translation has considered words as a basic unit. This is mainly due to the fact that major components in the existing ...
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Multichannel Music Separation with Deep Neural Networks
... DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...voice and other instruments from a mixture containing multiple musical ...evaluation, and estimating the optimal ...
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