[PDF] Top 20 Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks
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Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks
... factorization with an expressive connectionist model that can describe long-term dependencies and high-level structure in the ...data. Recurrent neural networks (RNN) (Rumelhart et ... Voir le document complet
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Recurrent Kernel Networks
... for sequences by allowing gaps in motifs, motivated by genomics ...a recurrent network, which we call recurrent kernel network ...sequence neural network, which was a source of inspiration for ... Voir le document complet
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Speech synthesis using recurrent neural networks
... unconditional audio generation based on generating one audio sample at a ...stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of ... Voir le document complet
74
From dynamics to computations in recurrent neural networks
... in networks with low-rank connectivity ...are high-dimensional, distributed and mixed, while the computations are based on low-dimensional dynamics on these represen- ...in ... Voir le document complet
236
Structured prediction and generative modeling using neural networks
... utilize neural networks to effectively model data with sequen- tial ...include audio, images, and ...of neural networks which find common use in both prediction and generative ... Voir le document complet
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Emotion Recognition with Deep Neural Networks
... by high dimensional binary ...image sequences, only the peak image is used in these experiments and the dynamic content is ...and audio and finding a way of combining them to reach a higher ... Voir le document complet
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Sequential modeling, generative recurrent neural networks, and their applications to audio
... but with real-valued data when it has been decided to bor- row the idea of output quantization for Audio Generation ...alongside with some of experiments for quantitative ...project with ... Voir le document complet
59
A mathematical approach to unsupervised learning in recurrent neural networks
... a neural network with unsupervised learning can create a model of its environ- ...learning neural network in mathematical terms from the observations of the biological mechanisms taking place in the ... Voir le document complet
279
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
... Due to the massive rise of hateful, abusive, offen- sive messages, social media platforms such as Twit- ter and Facebook have been searching for solutions to tackle hate speech (Lomas, 2016). As a conse- quence, the ... Voir le document complet
6
Recognizing flight manoeuvre with deep recurrent neural nets
... We will then describe several variants of DRNN to solve the problem,in particular we will describe a multi-output approach which allows to select several behaviours at the same time when it is not possible to ... Voir le document complet
2
The expressive power of analog recurrent neural networks on infinite input streams
... some neural language L ( N ) ⊆ { 0 , 1 } + , called the language recognized by N , and defined as the set of all finite words of bits that could be positively classified by N in some finite time of ... Voir le document complet
13
High dimensional Apollonian networks
... We denote the d-dimensional Apollonian network after t iterations by A(d, t), d ≥ 2, t ≥ 0. Then the d-dimensional Apollonian network at step t is constructed as follows: For t = 0, A(d, 0) is the complete ... Voir le document complet
10
Modeling orienting behavior and its disorders with "ecological" neural networks.
... However, with fewer hidden units the agents first oriented the central portion of their visual field to the peripheral stimulus, and only as a second step were they able to identify ... Voir le document complet
52
Towards better understanding and improving optimization in recurrent neural networks
... [24]) with 152 layers which was pre-trained on ImageNet for image ...LSTM with 512 hidden units for caption ...[36] with a learning rate of 10 −4 and leave the rest of the hyper-parameters as ... Voir le document complet
109
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
... 5 Conclusion In this paper, we have presented an approach based on recurrent neural networks (RNN) to induce multi- lingual text analysis tools. We have studied Simple and Bidirectional RNN ... Voir le document complet
12
Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
... 2 Leila Kerkeni and al. [2] and modulation spectral features (MSFs) [3]. Table 1 show the recognition rate for each combination of various features and classifiers for Berlin and Spanish databases. The overall ... Voir le document complet
3
Bandwidth extension of musical audio signals with no side information using dilated convolutional neural networks
... layers with filters of size (1, F ) and (T, 1) that independently summarize infor- mation in the frequency and time dimensions of the input data ...spectrum with the original input ...though with ... Voir le document complet
6
High Performance Associative Neural Networks: Overview and Library
... 2.1 Network architectures Associative networks can be designed using one of the following architectures. Fully-connected architecture. This architecture, often referred to as Hopfield-like, is studied the best in ... Voir le document complet
17
Thermodynamics-based Artificial Neural Networks for constitutive modeling
... by high accuracy of the predictions, higher than those of standard ...relation with the stresses, the Jacobian ∂σ ∆ε at the material point level is better predicted even for increments far beyond the ... Voir le document complet
46
Assessment of the modeling abilities of neural networks
... The approach to determine the relative efficacy of neural networks has been to compare the results of the neural network models from test bed problems with results from models[r] ... Voir le document complet
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