Top PDF Applications of complex numbers to deep neural networks
Applications of complex numbers to deep neural networks
... networks on toy ...Using complex weights in neural networks also has biological ...biologically-plausible deep network that allows one to construct richer and more versatile ...
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Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks
... is to use frequency domain analysis, which directly involves complex ...representation of a real-valued input is obtained using the short time Fourier transformation which is a mapping from the set ...
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Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications
... participated to an intelligibility ...consisted of 10 French vowels /a/, /i/, /u/, /o/, /œ/, /e/, /y/, /ã/, /ɛ̃/, /ɔ̃/, and 30 vowel- consonant-vowel (VCV) pseudo words made of the 10 consonants /p/, ...
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Deep Background Subtraction with Scene-Specific Convolutional Neural Networks
... vision applications such as traffic monitoring, people counting, and action ...approach to this problem is background subtraction, which has been extensively studied in the literature over the last two ...
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Contributions to handwriting recognition using deep neural networks and quantum computing
... number of visible particles in the universe for a reasonably-sized quantum computer of several hundreds of ...want to highlight the fact that decades of engineering have gone into ...
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A brief introduction to multichannel noise reduction with deep neural networks
... HAL Id: hal-02506387 https://hal.inria.fr/hal-02506387 Submitted on 5 Jun 2020 HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they ...
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Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications
... framework of the video hyperlinking task using the Me- diaEval 2014 dataset and the respective groundtruth that was collected as part of the challenge ...segments of interest within videos that a ...
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Singing voice detection with deep recurrent neural networks
... characteristics of singing voice: vibrato and tremolo. In order to improve state-of-the-art results, current singing voice detection techniques usually focus on the feature ...is to combine a ...
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Contributions to handwriting recognition using deep neural networks and quantum computation
... competitiveness of CRNNs (particularly against MDLSTMs) has been convincingly demonstrated in the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset [181], targeting the adaptation of ...
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New Paradigm in Speech Recognition: Deep Neural Networks
... recognition, deep neural network, acoustic modeling ...hours of multimedia is uploaded per minute ...companies to view, analyze, and mine the huge amount of multimedia data on the ...
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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] ...
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Impact of reverberation through deep neural networks on adversarial perturbations
... try to catego- rize inputs as clean or ...network to detect adversarial examples. For example, the authors of [ 22 ] trained on both clean and adversarial examples a sub-network classifier to ...
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Multichannel Music Separation with Deep Neural Networks
... use of DNNs for audio source separation by modeling the spectral informa- tion is extremely ...framework to exploit DNNs for multichannel audio source separation. Most of the approaches considered ...
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On the Expressive Power of Deep Fully Circulant Neural Networks
... used to approximate the Johson-Lindenstrauss transform, often used in machine learning to perform dimensionality ...proposed to replace the weight matrix of a fully connected layer by a ...
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Compression of Deep Neural Networks for Image Instance Retrieval
... quantization of weight parameters. Another approach to trade-off model size for performance is to prune entire convolutional layers of the network, motivated by the visualization work in ...
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Multichannel audio source separation with deep neural networks
... In summary, the proposed multichannel approach outper- formed single-channel DNN-based approach even when using DNN 0 only. The spatial parameters and their updates improved the enhancement performance. From the ...
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Deep convolutional neural networks to monitor coralligenous reefs: Operationalizing biodiversity and ecological assessment
... performances of the classifier ( Beijbom et ...integration of analyses made at different scales can help build a more robust and discriminative ...ensemble of ResNet18s on four patch sizes ( Figure 6 ...
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Classification of Time-Series Images Using Deep Convolutional Neural Networks
... convolutional deep belief network (CDBN) for audio ...proposed to deal with multivariate ...outputs of all CNNs are concatenated and fed into a fully connected MLP ...Instead of using the raw ...
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Entropy and mutual information in models of deep neural networks
... approximation of the tanh, hardtanh(x) = −1 for x<−1, x for −1<x<1, and 1 for x>1, for which the integrals in the replica formula can be evaluated faster than for the ...tendency of the replica ...
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Extensive deep neural networks for transferring small scale learning to large scale systems
... used to accurately learn the mapping from atomic coordinates to ...simulation? To investigate this we per- formed a Metropolis Monte Carlo (MC) simulation of pristine graphene, the same ...
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