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[PDF] Top 20 af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks

Has 10000 "af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks" found on our website. Below are the top 20 most common "af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks".

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

... 1 In recent years, deep learning has revolutionized the field of machine learning, for computer vision in ...particular. In this approach, a deep (multilayer) artificial ... Voir le document complet

24

Role of synaptic variability in resistive memory-based spiking neural networks with unsupervised learning

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 ... Voir le document complet

13

Learning Activation Functions in Deep Neural Networks

Learning Activation Functions in Deep Neural Networks

... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...of deep learning on images, video or text classification, ... Voir le document complet

171

Learning Sparse Filters In Deep Convolutional Neural Networks With A l 1 /l 2 Pseudo-Norm

Learning Sparse Filters In Deep Convolutional Neural Networks With A l 1 /l 2 Pseudo-Norm

... In this paper, we present a sparsity-inducing regularization term based on the ratio l 1 /l 2 pseudo-norm defined on the filter coefficients. By defin- ing this pseudo-norm appropriately for the different filter ... Voir le document complet

16

Deep neural networks with transfer learning in millet crop images

Deep neural networks with transfer learning in millet crop images

... Mr Dantouma Kamissoko is Lecturer with authority to direct research since 2003 at the Université de Bretagne Occidentale (UBO) held the following positions: 1991- 1995 head of department and research at the National ... Voir le document complet

7

New Paradigm in Speech Recognition: Deep Neural Networks

New Paradigm in Speech Recognition: Deep Neural Networks

... of deep neural networks ...flagship in the fields of artificial intelligence. Deep learning has surpassed state- of-the-art results in many domains: image recognition, ... Voir le document complet

8

Deep neural networks for choice analysis

Deep neural networks for choice analysis

... analyze in- dividual decision-making, by contrasting the subtractive perspective in DNNs to the additive perspective in classical choice ...understandable in the traditional setting because ... Voir le document complet

128

Unsupervised Layer-Wise Model Selection in Deep Neural Networks

Unsupervised Layer-Wise Model Selection in Deep Neural Networks

... investigate in more depth these findings, specifically examining the properties of abstraction of the hidden lay- ers in an Information Theoretical perspective and taking inspiration from ...lum ... Voir le document complet

7

Emotion Recognition with Deep Neural Networks

Emotion Recognition with Deep Neural Networks

... joint learning of features from different ...recurrent networks (Chen and Jin, 2015; He et ...feature learning stage is separate from the rest of the model. Joint learning might improve ... Voir le document complet

145

Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

... proposed deep neural network architecture with tied weights is trained bidirectionally from the first modality to the second and from the second modality to the first and cre- ates a crossmodal mapping ... Voir le document complet

5

Technical report: supervised training of convolutional spiking neural networks with PyTorch

Technical report: supervised training of convolutional spiking neural networks with PyTorch

... continuous-valued deep networks to spiking neural networks for image ...coding. Spiking neural networks can also be trained directly using spike-timing-dependent ... Voir le document complet

25

A Model-checking Approach to Reduce Spiking Neural Networks

A Model-checking Approach to Reduce Spiking Neural Networks

... biological networks is not new in systems ...found in [ Naldi et ...regulatory networks preserving some dynamical properties of the original models, such as stable states, in [ Gay et ... Voir le document complet

9

A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

A critical survey of STDP in Spiking Neural Networks for Pattern Recognition

... present in 2007 the motivation and main interest for combining STDP with temporal rank- order coding of information to promote unsupervised ...propagated in a discrete manner when real-life visual stream is ... Voir le document complet

10

Deep neural networks for audio scene recognition

Deep neural networks for audio scene recognition

... consists in determining automatically the context or environment around a device ...music. In this way, time- domain (zero-crossing rate), frequency-domain (band-energy ration, spectral centroid, spectral ... Voir le document complet

6

Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

... years, deep learning based approaches achieved outstanding results in image recognition and ...Among deep learning-based models for action recognition, Convolutional Neural ... Voir le document complet

7

Extensive deep neural networks for transferring small scale learning to large scale systems

Extensive deep neural networks for transferring small scale learning to large scale systems

... a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with O ðNÞ ...datasets. In the latter, an ... Voir le document complet

14

Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI

Learning Sparse deep neural networks using efficient structured projections on convex constraints for green AI

... that in practice, relatively few network weights are actually necessary to accurately learn data ...proposed in order to remove network weights (weight sparsification) either on pre-trained models or during ... Voir le document complet

9

Unsupervised post-tuning of deep neural networks

Unsupervised post-tuning of deep neural networks

... Supervised learning aims at minimizing the classifier error for a specific task, for instance predicting the sentiment from short texts or recognizing persons in ...fulfilled in practice, although ... Voir le document complet

9

Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

... controlled. Learning the parameters of a neural network model In biological context, learning is mainly related to synaptic plasticity [21, 16] and STDP (see ...as spiking neuron ... Voir le document complet

45

Comparing learned representations of deep neural networks

Comparing learned representations of deep neural networks

... features in in- put images, and have very high adversarial example ...desirable in certain use-cases where consistency is preferred, or particularly undesirable in avoiding adversarial example ... Voir le document complet

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