F
Farhad Arbabzadah
Researcher at Technical University of Berlin
Publications - 4
Citations - 1686
Farhad Arbabzadah is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Artificial neural network & Transfer of learning. The author has an hindex of 4, co-authored 4 publications receiving 1367 citations.
Papers
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Journal ArticleDOI
Quantum-chemical insights from deep tensor neural networks.
Kristof T. Schütt,Farhad Arbabzadah,Stefan Chmiela,Klaus R. Müller,Klaus R. Müller,Alexandre Tkatchenko,Alexandre Tkatchenko +6 more
TL;DR: In this article, a deep tensor neural network is used to predict atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure.
Journal Article
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Sch "utt,Farhad Arbabzadah,Stefan Chmiela,Klaus-Robert M "uller,Alexandre Tkatchenko +4 more
TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Book ChapterDOI
Identifying individual facial expressions by deconstructing a neural network
TL;DR: In this paper, the authors focus on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks and apply transfer learning with two base models to avoid overfitting.
Posted Content
Identifying individual facial expressions by deconstructing a neural network
TL;DR: In this paper, the authors focus on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using deep neural networks and apply transfer learning with two base models to avoid overfitting.