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HHTL proposes using a Deep Learning [68] approach to perform cross-domain feature mapping and distribution bias reduction.
You can never have too much compute power when you're running complex simulations or using a deep learning framework like TensorFlow, Torch, MXNet of Caffee.
To differentiate the visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from the VFs of healthy eyes using a deep learning (DL) method.
This solution is realized using a deep learning method employing a marginalized stacked denoised autoencoder as proposed by Chen [16] to learn the asymmetric transformation and the mapping to a common latent feature space.
In this paper, we explore this new level of image understanding with the ultimate goal of relating a set of image preferences to personality traits by using a deep learning framework.
Nvidia is using a deep learning approach, however, by providing its autonomous system with real-world data from humans drivers and letting it learn how to drive on its own – like a supercharged, AI-powered teenager getting behind the wheel using only their experience of being a passenger to guide them.
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Hinton et al. [29] use a Deep Learning and convolutional neural network for image object recognition.
We use a deep learning algorithm to learn the spectral and spatial features, and the number of hidden units is an important parameter.
In [16], the authors used a deep learning framework to learn pairs of mid-level filters which encode the transformation of mid-level appearance between the two cameras.
When images are uploaded to eBay, it uses a deep learning model called a convolutional neural network to process the images.
Like others at the frontiers of the field, NTechlab uses a deep learning approach, which clearly serves the purpose very well.
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