![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 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](https://www.pdfsearch.io/img/f84691dc07eafd65ea884ee017a6f73d.jpg) Date: 2015-07-26 20:01:45M-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 AmsterdamAdd to Reading ListSource URL: arxiv.orgDownload Document from Source Website File Size: 605,02 KBShare Document on Facebook
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