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Publications associées (28)

Lagrangian Discretization Of Crowd Motion And Linear Diffusion

Federico Stra

We study a model of crowd motion following a gradient vector field, with possibly additional interaction terms such as attraction/repulsion, and we present a numerical scheme for its solution through a Lagrangian discretization. The density constraint of t ...

SIAM PUBLICATIONS2020

Adaptive Gradient Descent without Descent

Konstantin Mishchenko

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don’t increase the stepsize too fast and 2) don’t overstep the local curvature. No need for functional values, no line search, no information about the func ...

2020

Gradient expansion formalism for magnetogenesis in the kinetic coupling model

Oleksandr Sobol

In order to describe magnetogenesis during inflation in the kinetic coupling model, we utilize a gradient expansion which is based on the fact that only long-wavelength (superhorizon) modes undergo amplification. For this purpose, we introduce a set of fun ...

American Physical Society2020

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems

Negar Kiyavash

Nonconvex minimax problems appear frequently in emerging machine learning applications, such as generative adversarial networks and adversarial learning. Simple algorithms such as the gradient descent ascent (GDA) are the common practice for solving these ...

2020

Electron paramagnetic resonance magnetic field sensors for particle accelerators

Giovanni Boero, Alessandro Valentino Matheoud, Anthony Jean Beaumont, Marco Buzio

We report on four electron paramagnetic resonance sensors for dynamic magnetic field measurements at 36 mT, 100 mT, 360 mT, and 710 mT. The sensors are based on grounded co-planar microwave resonators operating at about 1 GHz and 3 GHz, realized using prin ...

2020

The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Updates

Sai Praneeth Reddy Karimireddy, Sebastian Urban Stich

We analyze (stochastic) gradient descent (SGD) with delayed updates on smooth quasi-convex and non-convex functions and derive concise, non-asymptotic, convergence rates. We show that the rate of convergence in all cases consists of two terms: (i) a stocha ...

Microtome Publishing2020

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Colin Neil Jones, Emilio Maddalena

We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while ...

2019

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Rachid Guerraoui, El Mahdi El Mhamdi, Julien Michel Stainer, Peva François Blanchard

The present application concerns a computer-implemented method for training a machine learning model in a distributed fashion, using Stochastic Gradient Descent, SGD, wherein the method is performed by a first computer in a distributed computing environmen ...

2019

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