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neural networks (NN)

Optical neural networks: The 3D connection

Optical neural networks: The 3D connection

... https://doi.org/10.1051/photon/202010434 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted ...

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SoK: Cryptography for Neural Networks

SoK: Cryptography for Neural Networks

... as neural net- works (including deep learning), Bayesian networks, or classifiers are well-known ...on neural networks (NN) that are inspired by the architecture of neurons in the human ...

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Binary Graph Neural Networks

Binary Graph Neural Networks

... graph neural networks, and show that our approach allows us to closely match or equal the performance of floating- point models on difficult benchmarks, with significant re- ductions in memory consumption ...

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Parallel Implementations of Hopfield Neural Networks On GPU

Parallel Implementations of Hopfield Neural Networks On GPU

... of neural network, people trend to design various of implementations for general purpose, so the GPU is usually chosen as a typical SIMD architecture to do neural networks ...ART Neural ...

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Learning Activation Functions in Deep Neural Networks

Learning Activation Functions in Deep Neural Networks

... from neural networks using adaptive activation functions on Moview Review data, we design a series of experiments to apply our proposed activation functions in CNN ...

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Spiking neural networks based on resistive memory technologies for neural data analysis

Spiking neural networks based on resistive memory technologies for neural data analysis

... Finally, the capability of OxRAM to implement synapses reproducing both Long Term Plas- ticity (LTP) and Short Term Plasticity (STP) was studied. Therefore, a programming strategy was developed in order to impose a ...

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Unconstrained Monotonic Neural Networks

Unconstrained Monotonic Neural Networks

... Notably, we do not make use of a multi-scale architecture to train our model on MNIST. On this task, UMNN-MAF slightly outperforms all other models by a reasonable margin. Samples generated by a conditional model are ...

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Deep neural networks for choice analysis

Deep neural networks for choice analysis

... With better regularization methods, DNN architectures, hyperparameter tuning algorithms, statistical theoretical understanding, or larger sample sizes, DNNs can control its large estimation error, thus providing more ...

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Convolutional neural networks for atomistic systems

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 ...

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Factorized second order methods in neural networks

Factorized second order methods in neural networks

... Artificial neural networks are a powerful machine learning tool for modeling complex ...a neural network for a given task often reduces to minimizing a scalar function of several millions of ...

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Speech synthesis using recurrent neural networks

Speech synthesis using recurrent neural networks

... In deep learning and numerical optimization literature, several papers suggest using a diagonal approximation of the Hessian (second derivative matrix of the cost function with respect to parameters), in order to ...

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On challenges in training recurrent neural networks

On challenges in training recurrent neural networks

... This thesis focused on the challenges in training Recurrent Neural Networks (RNNs). Specifically, we attempted to tackle the following three fundamental chal- lenges that arise when training RNNs in ...

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Emotion Recognition with Deep Neural Networks

Emotion Recognition with Deep Neural Networks

... The second article, titled “Recurrent Neural Networks for Emotion Recognition in Video” (Ebrahimi Kahou et al., 2015), addresses the shortcomings of the previous article. Specifically, it introduces a ...

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A RANDOM MATRIX APPROACH TO NEURAL NETWORKS

A RANDOM MATRIX APPROACH TO NEURAL NETWORKS

... of neural network applications, the present article is merely a first step towards a better understanding of the “hardening” effect occurring in large dimensional networks with numerous samples and large ...

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Spectral-designed depthwise separable graph neural networks

Spectral-designed depthwise separable graph neural networks

... Graph Neural Networks (ConvGNNs) by designing new graph convolutions in spectral domain with a cus- tom frequency profile while applying them in the spatial ...

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Comparing learned representations of deep neural networks

Comparing learned representations of deep neural networks

... The choice of architecture, including number of layers, units per layer, and ac- tivation functions is made prior to training, and training of the weights and biases of the network can be performed using ...

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Probabilistic Robustness Estimates for Deep Neural Networks

Probabilistic Robustness Estimates for Deep Neural Networks

... x F is the gradient of F with respect to the network input x. The linear approximation (4) is only local but its advan- tage is that it can easily be evaluated at many x val- ues. Indeed, while visiting all input vectors ...

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Utilization of neural networks to expand databases

Utilization of neural networks to expand databases

... 2 National Research Council Canada, Morched.Zeghal@nrc.ca 1. Abstract This paper examines the potential use of the artificial neural networks (ANN) technique as a tool for expanding databases. The ...

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Modelling large neural networks via Hawkes processes

Modelling large neural networks via Hawkes processes

... real neural networks such as oscillations [ 21 , 22 ...huge neural networks as a whole, using PDE formalism [ 33 , 150 ...real networks, but once again there is no statistical proof of ...

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Deep neural networks for audio scene recognition

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), ...such networks can ...

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