In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies without prior knowledge of the dynamics of the task. However, obtaining successful policies required thousands to millions of training episodes, limiting the applicability of these approaches to real hardwa...

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure. These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization...

An approach to supervised training of document-specific character templates from sample page images and unaligned transcriptions is presented. The template estimation problem is formulated as one of constrained maximum likelihoodparameter estimation within the document image decoding (DID) framework. This leads to a two-phase iterative training algorithm consisting of transcriptionalignment and...

ABSTRACT Context: there is a certain difficulty for students in understanding what steps need to be followed guarantee that chosen research problem academically valid. There are also difficulties executing, training, and passing on the methodological procedure. Objective: present study aims detail operationalization of method identifying problems, allowing prove unique singular character their ...

MOTIVATION An accurate diagnostic and prediction will not be achieved unless the disease subtype status for every training sample used in the supervised learning step is accurately known. Such an assumption requires the existence of a perfect tool for disease diagnostic and classification, which is seldom available in the majority of the cases. Thus, the supervised learning step has to be condu...