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[PDF] Top 20 From dynamics to computations in recurrent neural networks

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From dynamics to computations in recurrent neural networks

From dynamics to computations in recurrent neural networks

... complex. In the Appendix C, we extend our analysis to positive input- output functions and show that little ...changes. In a first approximation, excitation-inhibition segregation corresponds ... Voir le document complet

236

Time-continuous Estimation of Emotion in Music with Recurrent Neural Networks

Time-continuous Estimation of Emotion in Music with Recurrent Neural Networks

... order to select useful features, we tested each feature type by adding them one at a time to the baseline feature ...found to improve the baseline CV performance: two variants of spectral flatness ... Voir le document complet

5

A Hierarchical Classification of First-Order Recurrent Neural Networks

A Hierarchical Classification of First-Order Recurrent Neural Networks

... first-order recurrent neural networks was also proved to intimately depend on both the choice of the activation function of the neurons as well as the nature of the synaptic weights under ... Voir le document complet

13

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

... models in a su- pervised way on datasets of musical pieces and their ...spectrogram to a multi-label classifier like the support vector machine (SVM) (Poliner and Ellis, 2007, 2005), a multilayer perceptron ... Voir le document complet

159

Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies

Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies

... Abstract. In this article, we present an original neural space/latency code, integrated in a multi-layered neural hierarchy, that offers a new perspective on probabilistic inference ...dynamic ... Voir le document complet

9

From Recurrent Neural Network to Long Short Term Memory Architecture

From Recurrent Neural Network to Long Short Term Memory Architecture

... performance in labelling and classification tasks. Recurrent neural networks (RNNs) do not suffer from these limita- tions, and would therefore seem a promising alternative to ... Voir le document complet

55

Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks

Sous-continents Estimation of Emotion in Music with Recurrent Neural Networks

... allowed to use our own acoustic features. To complete the 260 baseline features, a set of 29 acoustic feature types were extracted with the ESSEN- TIA toolbox [6], which is a toolbox specifically designed ... Voir le document complet

4

Segmentation and Classification of Opinions with Recurrent Neural Networks

Segmentation and Classification of Opinions with Recurrent Neural Networks

... information to companies, government and other entities, who want to understand the likes, dislikes and feedback of the users and people in ...analysis. In this paper, we address a problem ... Voir le document complet

9

Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks

Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks

... model In table 1 we report the results obtained for the unsupervised ...compared to those with two hidden lay- ...shown in the table, this accuracy is close to that of the simple pro- jection ... Voir le document complet

11

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

... (.) to be the Rectified Linear Unit (ReLU), inspired by its the recent success when training very deep structure in computer vision field (Nair & Hinton (2010); Krizhevsky et ...differs from the ... Voir le document complet

16

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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks

... tempts to create spiking versions of ...approach to structurally trans- lating an LSTM to a spiking version, the work of [228] took a reservoir inspired ...(connected to X, R, and Y ) with ... Voir le document complet

24

De-identification of patient notes with recurrent neural networks

De-identification of patient notes with recurrent neural networks

... (aside from labels required for evaluating the sys- tem), and are easy to implement, interpret, main- tain, and improve, which explains their large pres- ence in the industry (Chiticariu et ...need ... Voir le document complet

14

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks

... learning in random recurrent neural networks, with a generic Hebbian learning rule including passive forgetting and different time scales for neuronal activity and learning ...system ... Voir le document complet

33

Is it time to switch to Word Embedding and Recurrent Neural Networks for Spoken Language Understanding?

Is it time to switch to Word Embedding and Recurrent Neural Networks for Spoken Language Understanding?

... Feedforward Neural Network is the same as for the previous algorithm: a succession of independent and local ...decisions. In an RNN a recur- rence is added to allow the Neural Network ... Voir le document complet

6

Recurrent Kernel Networks

Recurrent Kernel Networks

... gaps in motifs, motivated by genomics ...a recurrent network, which we call recurrent kernel network ...arises from the dynamic programming structure used to compute efficiently the ... Voir le document complet

20

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

... depth in RNN could also refer to input-to-hidden, hidden-to-hidden or hidden-to-output ...deep in all of these senses. See Section 3.2. Recursive Neural Networks ... Voir le document complet

16

The expressive power of analog recurrent neural networks on infinite input streams

The expressive power of analog recurrent neural networks on infinite input streams

... the dynamics of the neural network is ...of neural network activity is usually carried out by multiple extracellular recordings of the time series of the neuronal ...steps in the range 0.1–1 ... Voir le document complet

13

InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks

InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks

... extracted from Wikipedia, where there are basically no ...frequent in social media data, and are often used to conveyed emo- tions and feelings associated with offenses or ironic ...needed to ... Voir le document complet

6

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

... 64. In [ 7 ] the authors showed that large finite size effects in the initialization affect the location of the BBP ...also in this case is unclear and deserves further ...presented in this ... Voir le document complet

19

Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks

Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks

... 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 apply them in the target ...results ... Voir le document complet

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