The scale-invariant model for the global extreme weather phenomenon variability – We present a novel class of stochastic methods for time series, which are designed to predict an outcome over time. With this model, a stochastic gradient descent algorithm is constructed. The proposed method is able to predict an outcome over time.
In this paper, we consider statistical learning which models a distribution whose value is dependent on the sample size or sample number and not on the distribution itself. We consider the problem of learning to learn a nonnegative matrix $mathcal{R}$ from data when the sample size or sample number is $O(n)$ such that the distribution is one of those $n$ distributions that is the least-squares distribution. We formulate the proposed learning problem as an adaptive sampling strategy which can be formulated as a linear time-series regression problem. We demonstrate the effectiveness of the proposed approach over a set of simulated and real data from the World Health Organization.
The success of deep neural networks can be attributed to their ability to discover more complex structures than existing ones due to its ability to extract useful local information. This paper considers the use of such data to design features of data structures. In this framework, the learning problem is formulated as a non-distributed tree-structured graph and its output is a function of the graph’s structure. This structure is used in the learning task to extract information about the network structure. To illustrate this concept, this research aims at developing a probabilistic parser for the tree-structured graph.
Learning Stochastic Gradient Temporal Algorithms with Riemannian Metrics
The scale-invariant model for the global extreme weather phenomenon variability
Learning how to model networks
Learning Feature Representations with Graphs: The Power of Variational InferenceThe success of deep neural networks can be attributed to their ability to discover more complex structures than existing ones due to its ability to extract useful local information. This paper considers the use of such data to design features of data structures. In this framework, the learning problem is formulated as a non-distributed tree-structured graph and its output is a function of the graph’s structure. This structure is used in the learning task to extract information about the network structure. To illustrate this concept, this research aims at developing a probabilistic parser for the tree-structured graph.