[PDF] Top 20 Structure-preserving neural networks
Has 9500 "Structure-preserving neural networks" found on our website. Below are the top 20 most common "Structure-preserving neural networks".
Structure-preserving neural networks
... physically-informed neural networks approach [ 40 , 48 ...employs neural networks to solve highly nonlinear partial differential equations (PDEs) resulting in very accurate and numerically ... Voir le document complet
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Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment
... Deep Neural Networks using punctual environmental information ...environmental structure and species dynamics [ 45 , 46 ], and on how space should be acknowledged in the analysis of biodiversity ... Voir le document complet
22
PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks
... privacy- preserving arrhythmia classification with neural ...their neural network is also composed of two layers: one hidden layer with SATLIN (a symmetric saturating linear) as an activation ... Voir le document complet
17
Music Structure Boundaries Estimation Using Multiple Self-Similarity Matrices as Input Depth of Convolutional Neural Networks
... Music Structure Boundaries Estimation with SSM and ConvNet ...music structure were mostly based on unsupervised learning algorithms: — clustering [9] or hidden Markov model [2] applied to audio signal ... Voir le document complet
9
Modelling the influence of data structure on learning in neural networks: the hidden manifold model
... the structure of real-world data sets is a major obstacle to the detailed theoretical understanding of deep neural ...two-layer neural networks trained on three different data sets: (i) an ... Voir le document complet
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SwaNN: Switching among Cryptographic Tools for Privacy-Preserving Neural Network Predictions
... 2 PRELIMINARIES Convolutional Neural Networks. CNNs are specif- ically designed for image recognition. They combine a series of layers to perform classification. The first layer of NN is the input layer, ... Voir le document complet
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Continuous-variable quantum neural networks
... building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in ... Voir le document complet
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Convolutional neural networks for atomistic systems
... (convolutional neural networks for atom- istic systems), for calculating the total energy of atomic systems which rivals the com- putational cost of empirical potentials while maintaining the accuracy of ab ... Voir le document complet
23
Spiking neural networks based on resistive memory technologies for neural data analysis
... elimination of synaptic connections which takes place mainly during early childhood and is a key mechanism for the beginning specialization of the CNS [39]. Synaptic plasticity is the ability of synapses to change their ... Voir le document complet
193
Speech synthesis using recurrent neural networks
... une structure hi´erarchique), est capable de capturer les sources de variation sous-jacentes dans les s´equences temporelles, et ce, sur de tr`es longs laps de temps, sur trois ensembles de donn´ees de nature ... Voir le document complet
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On challenges in training recurrent neural networks
... function preserving transformations, to achieve zero-shot knowledge transfer when expanding a small, trained network (referred to as the teacher network) into a large, untrained net- work (referred to as the ... Voir le document complet
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Parallel Implementations of Hopfield Neural Networks On GPU
... Nikola B. Serbedzija[ 7 ] made a summary about the techiques in the paralleli- sation of simulating the artificial neural network, especially in the specific parallel architectures. Several theoratical ... Voir le document complet
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ProteiNN: Privacy-preserving one-to-many Neural Network classifications
... on neural networks (NN). The problem of privacy-preserving NN classification has already been studied by many researchers (see (Azraoui et ... Voir le document complet
9
Optical neural networks: The 3D connection
... [8] T. Heuser, M. Pflüger, I. Fischer, J.A. Lott, D. Brunner, S. Reitzenstein, J. Phys. Photonics 2, 044002 (2020) Figure 5. (A) 3D rendering of the OVE in [4] with the ideal input and output pairs; (B) SEM image of the ... Voir le document complet
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Emotion Recognition with Deep Neural Networks
... Facetube extraction procedure For the competition dataset video frames were extracted preserving the original aspect ratio. Then the Google Picasa face detector (Google, 2013) was used to crop detected faces in ... Voir le document complet
145
Neural Networks for Complex Data
... artificial neural networks model described in the previous sections are ad hoc in the sense that they are constructed using specific features of the data at ...data structure that is very frequent in ... Voir le document complet
8
On Deep Multiscale Recurrent Neural Networks
... LSTMs ( Hochreiter and Schmidhuber , 1997 ) employ the multiscale update con- cept, where the hidden units have different forget and update rates and thus can operate with different timescales. However, unlike our model, ... Voir le document complet
144
af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks
... Spiking networks also have the advantage of being intrinsically sensitive to the temporal characteristics of information transmis- sion that occurs in the biological neural ...in neural coding [17], ... Voir le document complet
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Factorized second order methods in neural networks
... 1.4. Common types of neural networks 1.4.1. Multilayer perceptron We now define the simplest neural network structure called the perceptron [ Rosenblatt , 1961 ]. From an input data vector x, ... Voir le document complet
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Structure-Preserving Chosen-Ciphertext Security with Shorter Verifiable Ciphertexts
... ], structure-preserving encryption should make it pos- sible to efficiently and non-interactively prove possession of a valid ciphertext, which rules out the use of standard techniques – like hash functions ... Voir le document complet
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