Search for tag: "subspace"

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42:42 Tree-Projected Gradient Descent for Estimating… Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs - Zhou Fan	Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs - Zhou Fan Structural Breaks and Shape Constraints Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs Zhou Fan (Yale University) 17 May 2022 icms tree graph star edge sparsity theta kind gradient subspace loss idea algorithm projection space step question problem factor kinds edges From Iain Cms 0 likes 25 plays 0 Licence Type All rights reserved Language English Date Created May 19th, 2022
42:53 Shape Constraints Imposed by Linear Layers in… Shape Constraints Imposed by Linear Layers in Neural Networks - Rebecca Willett	Shape Constraints Imposed by Linear Layers in Neural Networks - Rebecca Willett Structural Breaks and Shape Constraints Shape Constraints Imposed by Linear Layers in Neural Networks Rebecca Willett (University of Chicago) 16 May 2022 icms function network layer representation networks layers weights functions samples linear training subspace cost units relu things alignment matrix space norm From Iain Cms 0 likes 35 plays 0 Licence Type All rights reserved Language English Date Created May 17th, 2022
24:12 Stephen Weiss Presentation - SSPD 2021 Stephen Weiss Presentation - SSPD 2021	Stephen Weiss Presentation - SSPD 2021 Presented at the Sensor Signal Processing for Defence Conference (SSPD) 2021Presentation: "Detection of Weak Transient Signals Using a Broadband Subspace Approach" Speaker: Stephan Weiss,… sspd 2021 matrix signal vector subspace covariance sources components source syndrome detection decomposition power sensor impulse distributions projection responses environment sensors signals From Joe Burchell 0 likes 18 plays 0 Licence Type All rights reserved Language English Date Created September 14th, 2021
52:41 One World: Stochastic Numerics and Inverse… One World: Stochastic Numerics and Inverse Problems - Raul Tempone (KAUST)	One World: Stochastic Numerics and Inverse Problems - Raul Tempone (KAUST) Subtitles will be added soon. One World: Stochastic Numerics and Inverse Problems Raul Tempone (KAUST) oneworld stochastic numerics and inverse problems seminar icms respect approximation level multi measure sampling variance samples rate context expectation cost quantity parameter process term sample carlo monte subspace thing delta variable From Liam Holligan 0 likes 34 plays 0 Publisher Liam Holligan Licence Type All rights reserved Language English Date Created September 22nd, 2021
48:49 James Pascoe Abstract preservers James Pascoe Abstract preservers	James Pascoe Abstract preservers Applied matrix positivity James Pascoe Abstract preservers This recording is in the process of being subtitled. Subtitles will be available in due course. applied matrix positivity sum sort cone structure invariant times function subspace theorem point thing space endomorphisms polynomials x. sets condition result proof algebra From Greg McCracken 0 likes 7 plays 0 Licence Type All rights reserved Language English Date Created July 21st, 2021
51:57 One World Virtual Seminar Series - Stochastic… One World Virtual Seminar Series - Stochastic Numerics and Inverse Problems: Svetlana Dubinkina	One World Virtual Seminar Series - Stochastic Numerics and Inverse Problems: Svetlana Dubinkina Svetlana Dubinkina, Vrije Universiteit Amsterdam - Shadowing approach to data assimilation Automatic subtitles have been added to this recording. We are editing the subtitles to reflect the speaker,… icms stochastic numerics data observations simulation observation error synchronisation cost trajectory approach subspace orbit solution conditions function condition curve dimension model method variables guess From Jane Walker 0 likes 85 plays 0 Publisher Diane Hoberry Licence Type All rights reserved Language English Date Created February 17th, 2021
01:51 When principal components fail When principal components fail	When principal components fail When principal components fail iaml pca attribute linear thing variance covariance dimension projection component matrix sheet subspace subspaces mind variances deviation distances fact gazillion attributes From Nigel Goddard 1 likes 2,041 plays 0 Licence Type Creative Commons - Attribution

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