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Probability theory, Probability, Nearest neighbor search, Discrete probability distribution, Probability distribution, Exponential distribution

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On Jun 19, 2020
@EvanKirstel shared
RT @jarokrolewski: We are announcing EMDE (Efficient Manifold Density Estimator) Establishing state-of-the art results & proving that we are absolutely one of the leader in building pragmatic #AI and #science driven (not PPT driven) solutions. more: https://t.co/8OQS5qKjtP #synerise 💪🔥🦾 https://t.co/LVsqlCY49L
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5 Ju n 2 02 0 An efficient manifold density estimator for all recommendation systems Jacek Da˛browski Synerise [email protected] Barbara Rychalska Synerise Warsaw University of Technology [email protected] Michał Daniluk Synerise [email protected] Dominika ...

arxiv.org
On Jun 19, 2020
@EvanKirstel shared
RT @jarokrolewski: We are announcing EMDE (Efficient Manifold Density Estimator) Establishing state-of-the art results & proving that we are absolutely one of the leader in building pragmatic #AI and #science driven (not PPT driven) solutions. more: https://t.co/8OQS5qKjtP #synerise 💪🔥🦾 https://t.co/LVsqlCY49L
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5 Ju n 2 02 0 An efficient manifold density estimator for all recommendation systems Jacek Da˛browski Synerise [email protected] Barbara Rychalska Synerise Warsaw University of ...

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A neural network catalyzer for multi-dimensional similarity search Alexandre Sablayrolles†,?, Matthijs Douze†, Cordelia Schmid?, and Hervé Jégou† †Facebook AI Research ?Inria Abstract This ...

Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions

Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability ...

Rethinking statistical learning theory: learning using statistical invariants

Rethinking statistical learning theory: learning using statistical invariants

This paper introduces a new learning paradigm, called Learning Using Statistical Invariants (LUSI), which is different from the classical one. In a classical paradigm, the learning machine ...

RMHMC_MG_BC_SC_REV_08_04_10.dvi

RMHMC_MG_BC_SC_REV_08_04_10.dvi

Metropolis Adjusted Langevin Algorithm Consider the random vector θ ∈ RD with density p(θ) and denote the log density as L(θ) ≡ log p(θ), then the Metropolis Adjusted Langevin Algorithm ...