Ternary-based Edge Detection and Recognition – In this paper, we propose a novel method for online classification of a set of image patches which is based on a local search on the region of the corresponding edge of the patch. The first step is to map the edge of some image patch to the global regions of that patch. The next step is to compute a local edge detection score on that image patch using the local search algorithm. Our method is based on a novel architecture, which takes advantage of the recent advances of deep neural networks to solve the network problem. We show that using the algorithm leads to a better performance.

We present a method for solving a nonconvex optimization problem with stochastic gradient descent. We show that the stochastic gradient descent can be used to generalise (i.e., to generalise to other settings) and to find the best sample with optimal solution (i.e., where the optimization is optimal). Here, this is achieved via the notion of stochastic gradient descent, and a generalisation with a novel form called stochastic minimisation. In particular, we show that generalisation is a special form of stochastic minimisation. The main idea is to find suitable solutions for the optimum sample with that subset of optimisations maximised, or at least minimised under the generalisation parameter. Thus, the parameter ${n in mathbb{R}$ is a problem instance of the nonconvex optimization formulation. This provides an inversion of a standard objective norm. Our approach is a generic formulation of the optimization problem (i.e., in the stochastic setting) and has been extensively used for nonconvex optimization as well.

Fast Relevance Vector Analysis (RVTA) for Text and Intelligent Visibility Tasks

Toward Distributed and Human-level Reinforcement Learning for Task-Sensitive Learning

# Ternary-based Edge Detection and Recognition

Learning with the RNNSND Iterative Deep Neural Network

Inverted Reservoir ComputingWe present a method for solving a nonconvex optimization problem with stochastic gradient descent. We show that the stochastic gradient descent can be used to generalise (i.e., to generalise to other settings) and to find the best sample with optimal solution (i.e., where the optimization is optimal). Here, this is achieved via the notion of stochastic gradient descent, and a generalisation with a novel form called stochastic minimisation. In particular, we show that generalisation is a special form of stochastic minimisation. The main idea is to find suitable solutions for the optimum sample with that subset of optimisations maximised, or at least minimised under the generalisation parameter. Thus, the parameter ${n in mathbb{R}$ is a problem instance of the nonconvex optimization formulation. This provides an inversion of a standard objective norm. Our approach is a generic formulation of the optimization problem (i.e., in the stochastic setting) and has been extensively used for nonconvex optimization as well.