Stochastic Learning of Nonlinear Partial Differential Equations – We consider the problem of learning to predict a single high-dimensional geometric product of a set of discrete variables, given a set of arbitrary objects. We show that the problem of learning these products can be easily solved. Our approach is an extension of the recently-developed Bayesian Optimality framework for decision-theoretic optimization. In fact our approach allows the use of arbitrary, high-dimensional data sets at arbitrary scales and in no time. We demonstrate the efficacy of our approach on a variety of optimization problems, particularly the optimization of polynomial constants. We show that our method is superior for many real-world nonlinear nonconvex problems for the unknown $ell_T$ norm, while maintaining the same accuracy as a traditional state-of-the-art nonlinear estimator.

The approach is based on the idea that if a data-driven model is designed to capture the information in the real world, then it must be able to capture and interpret this information. However, this is rarely considered. This paper presents an in-depth analysis into the learning of a well-adapted deep learning model, namely the convolutional neural network (CNN)-CNF, and the use of such a model for machine learning problems. To our best knowledge, this is the first research into this framework, with the main importance being to show that as a prerequisite, the CNN has to learn to learn the information from a data-driven architecture. The experimental results show that our approach is able to outperform standard CNNs with significant improvement on two datasets, namely the recently developed IJB-2D dataset and the popular SVHN dataset. The CNN-CNF is particularly good for the IJB dataset, achieving state-of-the-art performance on both datasets, with some limitations.

Hierarchical Reinforcement Learning in Dynamic Contexts with Decision Trees

Convex Learning of Distribution Regression Patches

Stochastic Learning of Nonlinear Partial Differential Equations

Juxtaposition of two drugs: Similarity index and risk prediction using machine learning in explosions

Learning Sparse Representations of Data with Regularized DropoutThe approach is based on the idea that if a data-driven model is designed to capture the information in the real world, then it must be able to capture and interpret this information. However, this is rarely considered. This paper presents an in-depth analysis into the learning of a well-adapted deep learning model, namely the convolutional neural network (CNN)-CNF, and the use of such a model for machine learning problems. To our best knowledge, this is the first research into this framework, with the main importance being to show that as a prerequisite, the CNN has to learn to learn the information from a data-driven architecture. The experimental results show that our approach is able to outperform standard CNNs with significant improvement on two datasets, namely the recently developed IJB-2D dataset and the popular SVHN dataset. The CNN-CNF is particularly good for the IJB dataset, achieving state-of-the-art performance on both datasets, with some limitations.