Results 281 to 290 of about 142,362 (315)

Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model. [PDF]

PLoS Comput Biol
Mondal A, Anirudh R, Selvaraj P.
europepmc   +1 more source

Mirror Descent and Exponentiated Gradient Algorithms Using Trace-Form Entropies. [PDF]

Entropy (Basel)
Cichocki A, Tanaka T, Nielsen F, Cruces S.   +3 more
europepmc   +1 more source

Error Analysis of Stochastic Gradient Descent Ranking

IEEE Transactions on Cybernetics, 2013
Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper.
Chen, Hong, Tang, Yi, Li, Luoqing, Yuan, Yuan, Li, Xuelong, Tang, Yuanyan   +5 more
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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.
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Stochastic Gradient Descent

2017
This chapter gives a broad overview and a historical context around the subject of deep learning. It also gives the reader a roadmap for navigating the book, the prerequisites, and further reading to dive deeper into the subject matter.
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Ant Colony Optimization and Stochastic Gradient Descent

Artificial Life, 2002
In this article, we study the relationship between the two techniques known as ant colony optimization (ACO) and stochastic gradient descent. More precisely, we show that some empirical ACO algorithms approximate stochastic gradient descent in the space of pheromones, and we propose an implementation of stochastic gradient descent that belongs to the ...
Meuleau, Nicolas, Dorigo, Marco
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Stochastic Gradient Descent with GPGPU

2012
We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on the GPU, achieving 1.66 to 6-times accelerations compared to a CPU-based implementation. The reference implementations are the Support Vector Machine by Bottou and the BRISMF predictor from the Netflix Prices winning team ...
David Zastrau, Stefan Edelkamp
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Resampling Stochastic Gradient Descent Cheaply

2023 Winter Simulation Conference (WSC), 2023
Henry Lam, Zitong Wang
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Stochastic Gradient Descent

2021
openaire   +1 more source