[PDF] Top 20 Contributions to handwriting recognition using deep neural networks and quantum computation
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Contributions to handwriting recognition using deep neural networks and quantum computation
... Zhang, and Y. Zhang, “Theano: A Python framework for fast computation of mathematical expressions,” may ...Vedral, and M. Gu, “Using quantum theory to reduce the complexity of ... Voir le document complet
196
Contributions to handwriting recognition using deep neural networks and quantum computing
... Zhang, and Y. Zhang, “Theano: A Python framework for fast computation of mathematical expressions,” may ...Vedral, and M. Gu, “Using quantum theory to reduce the complexity of ... Voir le document complet
196
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
... [12–16]) and recent, related models in the machine learning community, generally referred to as ‘‘deep neural networks’’ share many properties with these bio-inspired ...back to ... Voir le document complet
19
Acoustic models for speech recognition using Deep Neural Networks based on approximate math
... out to have the same effect as the non-Kahan ...rule and simplifies the resulting expression ...compilers and of the Theano optimizer. Fortunately, the C, C++, and Cuda compilers do not apply ... Voir le document complet
83
Energy Efficient Techniques using FFT for Deep Convolutional Neural Networks
... convolutional neural networks (CNNs) has been developed for a wide range of applications such as image recognition, nature language processing, ...of deep CNNs in home and mobile ... Voir le document complet
7
Dynamic Bayesian Networks for Integrated Neural Computation
... designed to be open to evolutions of the knowledge in neuropsychology and ...us to model the brain as a dynamic causal probabilistic network with nonlinear ...between and inside the ... Voir le document complet
5
Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks
... convolutional neural network trained on LBM-generated ...1 to 4 2D Gaussian ...proposed to increase the accuracy of the predictions. Both networks are then evaluated with initial conditions ... Voir le document complet
19
Deep neural network adaptation for children's and adults' speech recognition
... best recognition per- formances when the operating (or testing) condi- tions are consistent with the training ...conditions. To be effective, the general training procedure de- scribed above requires that a ... Voir le document complet
6
Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications
... evaluation using behavioral testing Eleven subjects participated to an intelligibility .../ɔ̃/, and 30 vowel- consonant-vowel (VCV) pseudo words made of the 10 consonants /p/, /t/, /k/, /f/, /s/ /ʃ/, ... Voir le document complet
6
Deep neural networks for choice analysis
... DNN to choice analysis, including the tension between domain-specific knowledge and generic-purpose mod- els, and the lack of interpretability and effective regularization ...contrast ... Voir le document complet
128
Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
... Kerkeni and al. [2] and modulation spectral features (MSFs) ...the recognition rate for each combination of various features and classifiers for Berlin and Spanish ...MFCC and MS ... Voir le document complet
3
Reconstructing faces from fMRI patterns using deep generative neural networks
... difficult to distinguish visually similar inputs, such as different ...developed deep learning system to reconstruct face images from human ...(VAE) neural network using a GAN ... Voir le document complet
11
Quantization and Deployment of Deep Neural Networks on Microcontrollers
... learning and hardware design. Presently, deep neural networks can be deployed on embedded targets to perform different tasks such as speech recognition, object detection or Human ... Voir le document complet
33
Using neural networks to describe tracer correlations
... The neural network used to produce the CH4 -N 2 O correlation in Panel (a) used Quickprop learning and one hidden layer with eight ...solution and the neural network solution was ...the ... Voir le document complet
5
Comparing learned representations of deep neural networks
... between networks as opposed to generalizable ...trained and pairs of ad- versarially trained networks is large when directly comparing the saliencies, but the difference is substantially ... Voir le document complet
64
Probabilistic Robustness Estimates for Deep Neural Networks
... difficult to say which regularizer performs ...methods and do not provide further explanation of this ...the neural network error surface may explain ...sufficiently to really express the ... Voir le document complet
10
Unsupervised post-tuning of deep neural networks
... instance to train a neural network on quantum physics experiments, where assigning a precise label to a given instance is not possible ...required to train any binary classifier, ... Voir le document complet
9
Multichannel Music Separation with Deep Neural Networks
... derived using the source spectra, which are estimated by DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...voice and other instruments from a mixture containing ... Voir le document complet
6
Learning Activation Functions in Deep Neural Networks
... increase and high-level layers exploit larger features which are produced by small parts of objects in lower levels and pass much more larger features to the subsequent layers as ...architecture ... Voir le document complet
171
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
... low- to high-level visual areas in both ventral and dorsal stream, in time over the visual processing stages in the first few hundred milliseconds of ...ship to emerge. Our results demonstrate the ... Voir le document complet
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