Search results with tag "Stochas tic"
Chapter 3 An Introduction to Stochastic Epidemic Models
eaton.math.rpi.eduAn Introduction to Stochastic Epidemic Models Linda J.S. Allen AbstractA brief introduction to the formulation of various types of stochas-tic epidemic models is presented based on the well-known deterministic SIS and SIR epidemic models. Three different types of stochastic model formu-
CHAPTER Logistic Regression
www.web.stanford.edu4.An algorithm for optimizing the objective function. We introduce the stochas-tic gradient descent algorithm. Logistic regression has two phases: training: we train the system (specifically the weights w and b) using stochastic gradient descent and the cross-entropy loss. test: Given a test example x we compute p(yjx) and return the higher ...
1 Frank-Wolfe algorithm
people.csail.mit.eduThe paper [3] shows a Frank-Wolfe method for the structured SVM, and derive a stochas-tic block coordinate descent method. This can be related to a stochastic gradient method in the primal. 4.2 Herding Problem In the herding problem, we are are given a set of samples x 1;::;x nand are trying to ap-
Learning Structured Output Representation using Deep ...
proceedings.neurips.ccposterior inference. However, the parameters of the VAE can be estimated efficiently in the stochas-tic gradient variational Bayes (SGVB) [16] framework, where the variational lower bound of the log-likelihood is used as a surrogate objective function. The variational lower bound is written as: logp (x) = KL(q ˚(zjx)kp (zjx))+E q ˚(zjx) logq ...
High-Frequency Component Helps Explain the Generalization ...
openaccess.thecvf.comprogressively, including studying the properties of stochas-tic gradient descent [31], different complexity measures [46], generalization gaps [50], and many more from differ-ent model or algorithm perspectives [30, 43, 7, 51]. In this paper, inspired by previous understandings that convolutional neural networks (CNN) can learn from con-
Rectified Linear Units Improve Restricted Boltzmann Machines
icml.ccRBMs were originally developed using binary stochas-tic units for both the visible and hidden layers (Hinton, 2002). To deal with real-valued data such as the pixel intensities in natural images, (Hinton & Salakhutdinov, 2006) replaced the binary visible units by linear units with independent Gaus-sian noise as first suggested by (Freund ...