Results 161 to 170 of about 1,424,164 (189)

Identifiable learning of dissipative dynamics. [PDF]

Nat Commun
Zhu A, Soh BW, Pavliotis GA, Li Q.
europepmc   +1 more source

Efficient Estimation Methods for the QR Distribution with Type-II Censored Data: An Empirical Validation on Lung Cancer Prognosis. [PDF]

Entropy (Basel)
Ramzan Q, Amin M, Alghamdi S, Alharbi R.
europepmc   +1 more source

Correction to "Pneumonia Detection in Veterinary X-Rays Utilizing PaddleSeg-Based Semantic Segmentation and Mamba-Powered Classification". [PDF]

Vet Radiol Ultrasound
europepmc   +1 more source

Conjugate Directions for Stochastic Gradient Descent

, 2002
The method of conjugate gradients provides a very effective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore ideas from conjugate gradient in the stochastic (online) setting, using fast Hessian-gradient products to set up low ...
Nicol N. Schraudolph, Thore Graepel
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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
openaire   +1 more source

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   +2 more sources

Stochastic Gradient Descent With Differentially Private Updates

, 2013
—Differential privacy is a recent framework for com-putation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are
Anand D Sarwate, Kamalika Chaudhuri
exaly   +2 more sources

Runtime Analysis of Stochastic Gradient Descent

Proceedings of the 4th International Conference on Computer Science and Application Engineering, 2020
Stochastic gradient descent (SGD) is one of the most famous methods for minimization. However, there are few results about the runtime analysis of SGD because of its randomness. In this paper, we explain how to approximate SGD by stochastic differential equations (SDE).
Guanqiang Hu, Yushan Zhang
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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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