[PDF] Top 20 A mathematical approach to unsupervised learning in recurrent neural networks
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A mathematical approach to unsupervised learning in recurrent neural networks
... is to introduce a mathematical theory to show how a neural network with unsupervised learning can create a model of its environ- ...consists in ... Voir le document complet
279
De-identification of patient notes with recurrent neural networks
... easy to implement, interpret, main- tain, and improve, which explains their large pres- ence in the industry (Chiticariu et ...need to be meticulously fine-tuned for each new dataset, are not robust ... Voir le document complet
14
Singing voice detection with deep recurrent neural networks
... machine learning techniques. They start by extracting a set of features from a short-term analysis of the audio signal and provide these features as an input to a classification system ... Voir le document complet
6
Unsupervised post-tuning of deep neural networks
... Terms—deep learning, unsupervised training, regular- ization, natural language processing ...NTRODUCTION A major challenge for deep learning classifiers is to move beyond traditional ... Voir le document complet
9
Designing Regularizers and Architectures for Recurrent Neural Networks
... known to be universal function approximators ...attributed to their empirical triumphs. Despite the success of a few fairly simple models and learning principles across a wide range of ... Voir le document complet
82
Towards better understanding and improving optimization in recurrent neural networks
... models to test our approach– the Show&Tell encoder-decoder model [67] which does not employ any attention mechanism, and the ‘Show, Attend and Tell’ model [71], which uses soft ...of a residual ... Voir le document complet
109
Unsupervised Layer-Wise Model Selection in Deep Neural Networks
... lead to a more parsimonious model. Further work will investigate in more depth these findings, specifically examining the properties of abstraction of the hidden lay- ers in an Information ... Voir le document complet
7
A Model-checking Approach to Reduce Spiking Neural Networks
... biological networks is not new in systems ...found in [ Naldi et al., 2011 ], where the authors propose a methodology to reduce regulatory networks preserving some dynamical ... Voir le document complet
9
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... deep neural network developed rapidly in recent years in both the field of computer vision and natural lan- ...propose a deep Convolutional Neural Net- works (CNN) with 8 layers ... Voir le document complet
16
Seismic velocity estimation: A deep recurrent neural-network approach
... was to propose a benchmark problem, simple enough so that dif- ferent NN designs can be tested at a small cost, but complex enough so that insights learned from it can be applicable to more ... Voir le document complet
10
A Hierarchical Classification of First-Order Recurrent Neural Networks
... tree in the Muller automaton A N of Figure ...proposes a new approach of neural computability from the point of view infinite word reading automata ...automata-theoretic to the ... Voir le document complet
13
Role of synaptic variability in resistive memory-based spiking neural networks with unsupervised learning
... variability in Resistive Memory-based Spiking Neural Networks with unsupervised learning 2 ...elements in artificial bio-inspired Spiking Neural Networks ...as ... Voir le document complet
13
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex
... used in the ...as a transition function from a spacially small state to a spacially large ...denotes a BN-ReLU-Conv/Deconv-BN-ReLU-Conv/Deconv pipeline (similar to ... Voir le document complet
16
Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks
... ABSTRACT In this paper, we describe the IRIT’s approach used for the MediaEval 2015 ”Emotion in Music” ...was to predict two real-valued emotion dimensions, namely valence and arousal, ... Voir le document complet
4
Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... Abstract. Unsupervised discovery of sub-lexical units in speech is a problem that currently interests speech ...researchers. In this paper, we report experiments in which we use phone ... Voir le document complet
13
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
... guage in German tweets: one discrim- inates between offensive and not offen- sive messages, and the second performs a fine-grained classification by recognizing also classes of ...same approach, ... Voir le document complet
6
Assembly output codes for learning neural networks
... PERSPECTNES A way to represent categories in multi-class problems is presented, that departs itself from the usual "grandmother cell" ...do a better job as output for a ... Voir le document complet
6
Unsupervised Speech Unit Discovery Using K-means and Neural Networks
... pseudo-words in Sect. 4.2 . It is interesting to compare the results with the ones obtained in a super- vised learning ...results in terms of ...tering approach. Figure 3 ... Voir le document complet
14
Learning Activation Functions in Deep Neural Networks
... deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...are a lot of studies and practical applications of deep learning on images, ... Voir le document complet
171
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
... representation learning methods have been proposed to avoid using such pre-processed and noisy alignments for label ...used to train NLP tools by exploiting labeled data from the source language and ... Voir le document complet
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