Deep neural network
Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
... where h, m 1 and m 2 are unknown hardening parameters to be inferred. The unknown material parameters E 0 , σ Y0 , h, m 1 , m 2 and the effect of the effective fiber aspect ratio Ar will be identified by Bayesian ...
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Representativity and Consistency Measures for Deep Neural Network Explanations
... f . This constant certifies that the gradients of the function represented by the deep neural network are bounded (given a norm) and that this bound is known. This robustness cer- tificate proves to ...
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End-to-End Deep Neural Network Design for Short-term Path Planning
... a deep neural network architecture inspired by DroNet for short-term path planning which is to predict a sequence of steering angles directly from an image obtained by forward camera, hence an ...
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Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Performance
... United States, 2 Speech and Hearing Biosciences and Technology, Harvard Medical School, Boston, MA, United States Many individuals struggle to understand speech in listening scenarios that include reverberation and ...
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Physics-based Deep Neural Network for Augmented Reality during Liver Surgery
... In such a pipeline we propose to accelerate the FE computation step by replacing it with a non-linear dimensionality reduction technique based on a U- Net architecture. Dimensionality reduction techniques have shown real ...
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One Versus all for deep Neural Network Incertitude (OVNNI) quantification
... for deep Neural Network Incertitude (OVNNI) quantification Gianni Franchi Andrei Bursuc Emanuel Aldea S´everine Dubuisson Isabelle Bloch Abstract—Deep neural networks (DNNs) are ...
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TRADI: Tracking deep neural network weight distributions
... a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss ...Keywords: Deep neural networks, weight distribution, ...
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Characterization of deep neural network feature space for inverse synthetic aperture radar automatic target recognition
... trained neural networks to classify different targets at sea based on inverse synthetic aperture radar (ISAR) ...these neural network based automatic target recognition (ATR) ...train neural ...
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RED-NN: Rotation-Equivariant Deep Neural Network for Classification and Prediction of Rotation
... Fontainebleau, France Abstract In this work, we propose a new Convolutional Neural Network (CNN) for classifica- tion of rotated objects. This architecture is built around an ordered ensemble of oriented ...
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Representational Content of Oscillatory Brain Activity during Object Recognition: Contrasting Cortical and Deep Neural Network Hierarchies
... DNN RDMs The MEG phase/power representations were also com- pared with representations in seven DNNs (so as to ensure that conclusions were not dependent on one spe- cific network architecture): AlexNet ( ...
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Architecture design for highly flexible and energy-efficient deep neural network accelerators
... accounts for the impact of the finite bandwidth on performance, i.e., it is optimized for the height of the colored-only bar. First, we compare only the number of active PEs, i.e., total bar height, of the two ...
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A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution
... The approach described in this paper is to use a regres- sion algorithm that is implemented in a feed-forward neural network with six hidden layers trained on a very large data set, consisting of Monte ...
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Deep-neural network approaches for speech recognition with heterogeneous groups of speakers including children
... Training a DNN is a difficult tasks mainly because the optimisation criterion in- volved is non convex. Training a randomly initialised DNN with back-propagation would converge to one of the many local minima involved in ...
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DNNZip: Selective Layers Compression Technique in Deep Neural Network Accelerators
... inference for different network models 1 . As it can be observed, the overall inference latency is due to the memory and on-chip communication latency. The overall inference energy is mainly due to memory (both ...
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Deep neural network based multichannel audio source separation
... where deep neural networks (DNNs) are used to model the source spectra and combined with the classical multichannel Gaussian model to exploit the spatial ...
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Deep neural network adaptation for children's and adults' speech recognition
... hybrid deep neural net- work (DNN) - hidden Markov model (HMM) approach for automatic speech recognition (ASR) to target groups of speakers of a specific ...
6
Accurate small bowel lesions detection in wireless capsule endoscopy images using deep recurrent attention neural network
... B. Deep learning methods Over the past 4 years, new algorithms based on deep neural networks have been developed to segment and detect lesions from WCE ...a network based on an architecture ...
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On Deep Multiscale Recurrent Neural Networks
... 2 Deep Learning Deep learning is a research field aiming at learning multiple levels of abstraction and feature representation for data using deep neural networks ( Bengio , 2009 ; Le- Cun et ...
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Comparing learned representations of deep neural networks
... of deep neural network architectures have obtained substan- tial accuracy improvements in tasks such as image classification, speech recognition, and machine translation, yet little is known about ...
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Emotion Recognition with Deep Neural Networks
... our deep neural network has helped us to avoid overfitting to the provided challenge ...the network directly on extracted faces from the challenge ...
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