This video shows the progression of the gradient-descent-algorithm for the example described in Fig. 4 of the manuscript. The example shows convergence of the minimum residual approach to the parameters of the scene based on coherence measurements
We present a novel approach to improve temporal coherence in Monte Carlo renderings of animation seq...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
The article investigated a modification of stochastic gradient descent (SGD), based on the previousl...
International audienceWe propose an optimization method obtained by the approximation of a novel dis...
International audienceThe aim of this article is to study the properties of the sign gradient descen...
In this lesson you'll learn about how to apply the gradient decent/ascent method to find optimum min...
Left panel: evolution of the perceptual distance maximized (red curve) or minimized (blue curve) fro...
This paper studies three related algorithms: the (traditional) gradient descent (GD) algorithm, the ...
© 2018 Curran Associates Inc..All rights reserved. Classically, the time complexity of a first-order...
The basic back-propagation learning law is a gradient-descent algorithm based on the estimation of t...
In this paper, we provide new results and algorithms (including backtracking versions of Nesterov ac...
Nous établissons un théorème de convergence locale de l'algorithme classique d'optimisation de systè...
We review recent works (Sarao Mannelli et al 2018 arXiv:1812.09066, 2019 Int. Conf. on Machine Learn...
Abstract: Stochastic gradient descent is an optimisation method that combines classical gradient des...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
We present a novel approach to improve temporal coherence in Monte Carlo renderings of animation seq...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
The article investigated a modification of stochastic gradient descent (SGD), based on the previousl...
International audienceWe propose an optimization method obtained by the approximation of a novel dis...
International audienceThe aim of this article is to study the properties of the sign gradient descen...
In this lesson you'll learn about how to apply the gradient decent/ascent method to find optimum min...
Left panel: evolution of the perceptual distance maximized (red curve) or minimized (blue curve) fro...
This paper studies three related algorithms: the (traditional) gradient descent (GD) algorithm, the ...
© 2018 Curran Associates Inc..All rights reserved. Classically, the time complexity of a first-order...
The basic back-propagation learning law is a gradient-descent algorithm based on the estimation of t...
In this paper, we provide new results and algorithms (including backtracking versions of Nesterov ac...
Nous établissons un théorème de convergence locale de l'algorithme classique d'optimisation de systè...
We review recent works (Sarao Mannelli et al 2018 arXiv:1812.09066, 2019 Int. Conf. on Machine Learn...
Abstract: Stochastic gradient descent is an optimisation method that combines classical gradient des...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
We present a novel approach to improve temporal coherence in Monte Carlo renderings of animation seq...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
The article investigated a modification of stochastic gradient descent (SGD), based on the previousl...
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