Results 131 to 140 of about 141,184 (170)

Fast PET reconstruction with variance reduction and prior-aware preconditioning. [PDF]

Front Nucl Med
Ehrhardt MJ, Kereta Z, Schramm G.
europepmc   +1 more source

Optimization of the cut configuration for skin grafts. [PDF]

Biomech Model Mechanobiol
Harbrecht H, Karnaev V.
europepmc   +1 more source

Tropical gradient descent. [PDF]

J Glob Optim
Talbut R, Monod A.
europepmc   +1 more source

JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics. [PDF]

Nat Methods
Deistler M, Kadhim KL, Pals M, Beck J, Huang Z, Gloeckler M, Lappalainen JK, Schröder C, Berens P, Gonçalves PJ, Macke JH.   +10 more
europepmc   +1 more source

A novel distributed gradient algorithm for composite constrained optimization over directed network. [PDF]

Sci Rep
Ou M, Zhang H, Yan Z, Yang Z, Ran H.
europepmc   +1 more source

Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling. [PDF]

Bull Math Biol
Gwiazda P, Kazarnikov A, Marciniak-Czochra A, Szymańska Z.   +3 more
europepmc   +1 more source

Impact of measurement noise on escaping saddles in variational quantum algorithms. [PDF]

Sci Rep
Kaminishi E, Mori T, Sugawara M, Yamamoto N.   +3 more
europepmc   +1 more source

Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage. [PDF]

Sci Rep
Lee RW, Patil AS, Zhang Y.
europepmc   +1 more source

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Stochastic gradient descent possibilistic clustering

11th Hellenic Conference on Artificial Intelligence, 2020
Although online versions of several well known clustering algorithms have been proposed, in order to deal effectively with the big data issue, as well as with the case where the data are available in a streaming fashion, very few of them follow the stochastic gradient descent philosophy.
Aggeliki Koutsibella, Konstantinos D. Koutroumbas   +1 more
openaire   +1 more source

Embedding Simulated Annealing within Stochastic Gradient Descent

2021
We propose a new metaheuristic training scheme for Machine Learning that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way. Our idea is to define a discrete neighborhood of the current SGD point containing a number of “potentially good moves” that exploit gradient information, and to search this neighborhood ...
Fischetti M., Stringher M.
openaire   +1 more source