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15:30 AML: Optimisation - Stochastic Gradient Descent AML: Optimisation - Stochastic Gradient Descent	AML: Optimisation - Stochastic Gradient Descent Applied Machine Learning - INFR11211 AML: Optimisation - Stochastic Gradient Descent Part 3/3 gradient descent points point things direction gradients error minimum function step dataset problems estimate weight variance estimator space sum loss From Siddharth N 0 likes 181 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
15:58 AML: Optimisation - Gradient Descent AML: Optimisation - Gradient Descent	AML: Optimisation - Gradient Descent Applied Machine Learning - INFR11211 AML: Optimisation - Gradient Descent Part 2/3 aml machine learning optimisation gradients function gradient step direction error size weight times algorithm rate point weights optimization region learning perturbation steps taylor descent From Siddharth N 0 likes 203 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
10:18 AML: Optimisation - ML Optimisation AML: Optimisation - ML Optimisation	AML: Optimisation - ML Optimisation Applied Machine Learning - INFR11211 AML: Optimisation - ML Optimisation Part 1/3 aml machine learning optimisation error function log gradient weights regression things weight edit probability linear perspective model direction values vector settings errors points functions From Siddharth N 0 likes 206 plays 0 Course Code INFR11211 Licence Type All rights reserved The University of Edinburgh
41:45 Sharp subelliptic estimates via an uncertainty… Sharp subelliptic estimates via an uncertainty principle - Gian Maria Dall'Ara	Sharp subelliptic estimates via an uncertainty principle - Gian Maria Dall'Ara Fourier Analysis @ 200 Sharp subelliptic estimates via an uncertainty principle Gian Maria Dall'Ara, Scuola Normale Superiore (Pisa) 30 June 2022 icms problem domain function solution boundary condition functions mapping bar point ellipticity domains delta disc regularity form l2 gradient variable origin From Iain Cms 0 likes 35 plays 0 Licence Type All rights reserved Date Created June 30th, 2022
01:49:29 Phase Behaviour, Biased Ensembles and Optimal… Phase Behaviour, Biased Ensembles and Optimal Control of Active Particles - Mike Cates	Phase Behaviour, Biased Ensembles and Optimal Control of Active Particles - Mike Cates From Individual to Collective Behaviour in Biological and Robotic Systems Phase Behaviour, Biased Ensembles and Optimal Control of Active Particles DAMTP, University of Cambridge Mike Cates icms density phase particles function particle rate equation model rho energy kind thing process potential separation gradient dynamics speed reversal theory From Iain Cms 0 likes 41 plays 0 Licence Type All rights reserved Date Created June 30th, 2022
30:32 Active Brownian Particles and chemotaxis - Oscar… Active Brownian Particles and chemotaxis - Oscar de Wit	Active Brownian Particles and chemotaxis - Oscar de Wit From Individual to Collective Behaviour in Biological and Robotic Systems Active Brownian Particles and chemotaxis University of Cambridge Oscar de Wit icms particles chemical particle model angle gradient linear bit field diffusion stability pde behaviour equation direction term process drift position sensitivity From Iain Cms 0 likes 61 plays 0 Licence Type All rights reserved Date Created June 29th, 2022
55:15 Flocks, mills, and platoons: from ad-hoc design… Flocks, mills, and platoons: from ad-hoc design to optimality-based formulation - Dante Kalise	Flocks, mills, and platoons: from ad-hoc design to optimality-based formulation - Dante Kalise From Individual to Collective Behaviour in Biological and Robotic Systems Flocks, mills, and platoons: from ad-hoc design to optimality-based formulation Imperial College London Dante Kalise icms control problem gradient optimization function conditions things instance dynamics equation optimality samples condition bit feedback consensus agents linear point agent From Iain Cms 0 likes 37 plays 0 Licence Type All rights reserved Date Created June 29th, 2022
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 30 plays 0 Licence Type All rights reserved Language English Date Created May 19th, 2022
47:03 Immersed Method with Metric Based Anisotropic Meh… Immersed Method with Metric Based Anisotropic Meh Adaptation for Computational Multiphase Flow Dynamics - Thierry Coupez	Immersed Method with Metric Based Anisotropic Meh Adaptation for Computational Multiphase Flow Dynamics - Thierry Coupez Adaptive Moving and Anisotropic Meshes for the Numerical Approximation of PDEs Immersed Method with Metric Based Anisotropic Meh Adaptation for Computational Multiphase Flow Dynamics Thierry… mesh pressure 3d edge fact ash gradient function bit metric amount solution direction jump visa size sickness equation stokes cylinder From Iain Cms 0 likes 32 plays 0 Licence Type All rights reserved Language English Date Created May 11th, 2022
51:45 One World SNIPS: Lukasz Szpruch One World SNIPS: Lukasz Szpruch	One World SNIPS: Lukasz Szpruch One World Virtual Seminar Series - Stochastic Numerics and Inverse Problems, 02 February 2022 Lukasz Szpruch (University of Edinburgh / Alan Turing Institute) From the theory of (stochastic)… stochastic numerics one world numerics control function measure gradient problem equation measures network model learning dynamics parameters guy problems layer sigma distribution cost flow functions From Simon Kershaw 0 likes 48 plays 0 Licence Type All rights reserved
24:08 Stefano Modena - Thursday 11 November Stefano Modena - Thursday 11 November	Stefano Modena - Thursday 11 November 08 Nov- 12 Nov 2021ICMS hosted the Convex Integration and Nonlinear Partial Differential Equations workshopNon-uniqueness for the transport equation with Sobolev vector fields Stefano Modena (TU -… icms convex integration pdes vector uniqueness field class equation space density infinity solutions question gradient fields condition lp cities solution transport incompressibility divergence talk From Simon Kershaw 0 likes 35 plays 0 Licence Type All rights reserved Language English Date Created November 11th, 2021
42:18 Angkana Ruland - Monday 8 November Angkana Ruland - Monday 8 November	Angkana Ruland - Monday 8 November 08 Nov- 12 Nov 2021 ICMS hosted the Convex Integration and Nonlinear Partial Differential Equations workshop Rigidity and Flexibility in the Modelling of Shape-Memory Alloys Angkana Ruland… ruland convex integration nonlinear solutions problem differential energy solution fact boundary inclusion phase flexibility model transformations regularity convex matrices picture gradient material structures matrix From Simon Kershaw 0 likes 79 plays 0 Licence Type All rights reserved
09:47 Anis Hamadouche Presentation - SSPD 2021 Anis Hamadouche Presentation - SSPD 2021	Anis Hamadouche Presentation - SSPD 2021 Presented at the Sensor Signal Processing for Defence Conference (SSPD) 2021Presentation: "Approximate Proximal-Gradient Methods" Speaker: Anis Hamadouche, Heriot-Watt… sspd 2021 error bounds rate convergence errors table analysis approximations precision algorithm function gradient bound assumptions problems optimization application step vector dimensionality From Joe Burchell 0 likes 45 plays 0 Licence Type All rights reserved Language English Date Created September 15th, 2021
09:13 Stefan Perko Student Talks Stefan Perko Student Talks	Stefan Perko Student Talks 30 Aug- 03 Sept 2021 ICMS hosted the European Summer School in Financial Mathematics 14th Edition Approximating stochastic gradient descent with diffusions: error expansions and impact of learning… icms perko stochastic gradient approximation gradient diffusion rate flow times sgd learning result age step phi sme loss term difference expectation sigma coefficient x. From Greg McCracken 0 likes 43 plays 0 Licence Type All rights reserved Date Created September 2nd, 2021
31:09 James Foster - Neural stochastic differential… James Foster - Neural stochastic differential equations for time series modelling	James Foster - Neural stochastic differential equations for time series modelling 30 Aug- 03 Sept 2021 ICMS hosted the European Summer School in Financial Mathematics 14th Ediition Neural stochastic differential equations for time series modelling James Foster (University… series modelling foster neural stochastic sd function loss distance question network networks model samples motion models series things discriminator gradient generator approach linear train solution From Greg McCracken 0 likes 35 plays 0 Licence Type All rights reserved
19:35 Bayesian Extremes: Rishikesh Yadav Bayesian Extremes: Rishikesh Yadav	Bayesian Extremes: Rishikesh Yadav This talk has been automatically captioned. You can remove these by pressing CC on the video toolbar. Name: Rishikesh Yadav Talk Title: A flexible Bayesian framework for modeling extreme… eva model models distribution gradient dependence beta vector alpha parameters tail mcmc scale copula independence mixture sites size process x2 diamonds From Belle Taylor 0 likes 24 plays 0 Publisher Centre for Statistics Licence Type All rights reserved Language English Date Created June 30th, 2021

Search for tag: "gradient"

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