Published as a conference paper at ICLRA DAM : A M ETHOD FOR S TOCHASTIC O PTIMIZATION Diederik P. Kingma* University of Amsterdam

First Page		Document Content
Date: 2015-07-26 20:01:45 M-estimators Computational statistics Computational neuroscience Estimation theory Stochastic optimization Stochastic gradient descent Deep learning Artificial neural network Convolutional neural network Mathematical optimization Loss function Feature learning		Published as a conference paper at ICLRA DAM : A M ETHOD FOR S TOCHASTIC O PTIMIZATION Diederik P. Kingma* University of Amsterdam Add to Reading List Source URL: arxiv.org Download Document from Source Website File Size: 605,02 KB Share Document on Facebook

	A Tensor Motion Descriptor Based on Multiple Gradient Estimators Dhiego Sad∗ , Virg´ınia F. Mota † , Luiz M. Maciel∗ , Marcelo B. Vieira ∗ , Arnaldo de A. Ara´ujo† ∗ Universidade Federal de Juiz de Fora IC DocID: 1ruWs - View Document
	Ann Inst Stat Math:469–490 DOIs10463Second-order asymptotic comparison of the MLE and MCLE of a natural parameter for a truncated exponential family of distributions DocID: 1reUL - View Document
	Comparing the resolution of Bartlett and MVDR estimators for bottom parameter estimation using pressure and vector sensor short array data DocID: 1r4kj - View Document
	Maximum Likelihood and Bayes Modal Ability Estimation in Two-Parametric IRT Models: Derivations and Implementation Norman Rose Institute of Psychology Friedrich Schiller University Jena DocID: 1r18B - View Document
	Estimating Nonlinear Additive Models with Nonstationarities and Correlated Errors1 Michael Vogt2,∗ Christopher Walsh3 DocID: 1qWUN - View Document