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In this work we design and train deep neural networks to predict topological invariants for one-dimensional four-band insulators in AIII class whose topological invariant is the winding number, and two-dimensional two-band ...

<jats:p>The remarkable practical success of deep learning has revealed some major surprises from a theoretical perspective. In particular, simple gradient methods easily find near-optimal solutions to non-convex optimization ...

This paper presents a comprehensive study on developing advanced deep learning approaches for nonlinear structural response modeling and prediction. Two schemes of the long short-term memory (LSTM) network are proposed for ...

© 2017 IEEE. Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local ...

Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works [1, 31] ...

We present deep Hubble Space Telescope, Chandra, Very Large Array, and Australia Telescope Compact Array images of the jets of PKS 0208-512 and PKS 1202-262, which were found in a Chandra survey of a flux-limited sample ...

We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, ...

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton–proton collisions at an energy of √ s = 13 TeV at the CERN LHC. The algorithm is trained on ...

Many individuals struggle to understand speech in listening scenarios that includereverberation and background noise. An individual’s ability to understand speech arisesfrom a combination of peripheral auditory function, ...

© 2020 Elsevier Ltd Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability ...