Published inTDS ArchiveMaking the “Automated scientist”: Co-navigating the hypothesis and experimental space using…Active learning approach co-navigates theory and experiment via hybrid of reinforcement learning and structured Gaussian ProcessesDec 14, 20211Dec 14, 20211
Published inTDS ArchiveUnknown Knowns, Bayesian Inference, and structured Gaussian ProcessesWhy domain scientists know more ML than they thinkNov 18, 20211Nov 18, 20211
Published inTDS ArchiveDeep Learning Meets Gaussian Process: How Deep Kernel Learning Enables Autonomous MicroscopyMaxim Ziatdinov¹ ² & Sergei V. Kalinin¹Nov 2, 2021Nov 2, 2021
Published inTDS ArchiveGaussian Process: First Step Towards Active Learning in PhysicsMaxim Ziatdinov¹ ² & Sergei V. Kalinin¹Nov 1, 2021Nov 1, 2021
Published inTDS ArchiveMastering the shifts with variational autoencodersHow variational autoencoders can be used to analyze one-dimensional signalsApr 13, 2021Apr 13, 2021
Published inTDS ArchiveEnter the j(r)VAE: divide, (rotate), and order… the cardsIntroduction to joint (rotationally-invariant) VAEs that can perform unsupervised classification and disentangle …Mar 11, 20211Mar 11, 20211
Published inTDS ArchiveHow we learnt to love the rotationally invariant variational autoencoders (rVAE), and (almost)…Introduction to unsupervised and class-conditioned variational autoencoders (VAEs) with rotational invariance and their application…Feb 27, 20211Feb 27, 20211