[PDF] Top 20 On the Expressive Power of Deep Fully Circulant Neural Networks
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On the Expressive Power of Deep Fully Circulant Neural Networks
... study deep fully circulant neural networks, that is deep neural networks in which all weight matrices are circulant ...these networks outperform ... Voir le document complet
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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks
... specific neural architectures demand new neuron models and learning ...distinguish the pattern of stimuli. The quest to meet these requirements can be accomplished by bio-inspired ... Voir le document complet
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Multichannel audio source separation with deep neural networks
... Preprocessing: The STFT coefficients were extracted using a Hamming window of length 1024 and hopsize 512 resulting F = 513 frequency ...bins. The time-varying time difference of arrivals ... Voir le document complet
14
Probabilistic Robustness Estimates for Deep Neural Networks
... pendent. The left hand side plots of Figure 1, represent- ing the validation mean absolute error, show that similar training performance were achieved by the four regulariz- ing methods and do ... Voir le document complet
10
Singing voice detection with deep recurrent neural networks
... at the upper one, cf. Section 3.1), the output of each hidden layer, the output of the network which is an estimation of the prob- ability of singing voice ... Voir le document complet
6
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] ... Voir le document complet
12
Fully Convolutional Neural Networks For Remote Sensing Image Classification
... classification, deep learning, convolutional neural ...pixel of an image. Contrary to the image categorization problem ...aspect of the dense classification problem is the ... Voir le document complet
5
Compression of Deep Neural Networks for Image Instance Retrieval
... some of the recent work on model compression, where the primary focus has been on reducing model size while maintaining high image classification ...[1], the fully connected layers ... Voir le document complet
11
New Paradigm in Speech Recognition: Deep Neural Networks
... recognition, deep neural network, acoustic modeling ...hours of multimedia is uploaded per minute ...mine the huge amount of multimedia data on the ...listening” of data ... Voir le document complet
8
Expressive power of pebbles Automata
... Besides the number of pebbles, there are other parameters of pebble automata that can be ...policies of lifting a pebble: in the original model [4], a pebble can be lifted only if it is ... Voir le document complet
13
Multichannel Music Separation with Deep Neural Networks
... where the multichannel filter is derived using the source spectra, which are estimated by DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...in the context ... Voir le document complet
6
Expressive power of query languages
... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ... Voir le document complet
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Impact of reverberation through deep neural networks on adversarial perturbations
... 2.1. The reverberation procedure as proposed in cognitive psychology In contrast to human brain, ANNs tend to forget their previous knowledge when learning new information, a well- known problem called ... Voir le document complet
10
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
... unfold The m-RNN model for one time frame 128 256 256 512 Figure 2: Illustration of the simple Recurrent Neural Network (RNN) and our multimodal Recurrent Neural Network (m-RNN) ...(a). ... Voir le document complet
16
Investigating the effect of DMRI signal representation on fully-connected neural networks brain tissue microstructure estimation
... 128, the best performing network on the synthetic dataset. the estimation of the most common brain microstructural fea- tures obtained from multi-compartmental ...on the use ... Voir le document complet
5
Are Topographic Deep Convolutional Neural Networks Better Models of the Ventral Visual Stream?
... predictivity of TDANNs A good model of the brain should also be able to predict human and monkey object discrimination ...on the task we evaluated (Rajalingham et ...one of the ... Voir le document complet
5
Entropy and mutual information in models of deep neural networks
... learning. The kernel- density approach of Kolchinsky ...mixture of Gaussians (MoG) to samples of the variable of interest and subsequently compute an upper bound on the ... Voir le document complet
66
Low-power neural networks for semantic segmentation of satellite images
... state-of-the-art neural net- works overlook the complexity of architectures, which typ- ically feature dozens of millions of trainable ...these networks requires ... Voir le document complet
9
Deep Background Subtraction with Scene-Specific Convolutional Neural Networks
... in the literature over the last two ...model of the static scene, which is named the background (BG) model, and comparing this model with the input ...constitute the ... Voir le document complet
5
Deep neural networks for audio scene recognition
... artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), ...In ... Voir le document complet
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