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PDF) Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
Deep learning for molecular design—a review of the state of the art - Molecular Systems Design & Engineering (RSC Publishing) DOI:10.1039/C9ME00039A
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules | ACS Central Science
Physics-Embedded Machine Learning for Electromagnetic Data Imaging
Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET | European Journal of Nuclear Medicine and Molecular Imaging
Frontiers | Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human
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Frontiers | Generative Models of Brain Dynamics
Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task | Nature Communications
Frontiers | Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models
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Data-Driven Strategies for Accelerated Materials Design | Accounts of Chemical Research
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Attri-VAE: Attribute-based interpretable representations of medical images with variational autoencoders - ScienceDirect
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Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real
