On a Generative Baseline for Modeling Clinical Trials – Converting a single model to a multiple model learning problem is a very challenging algorithm in practice. In contrast, an appropriate solution is a multi-model problem, which combines two distinct types of problems: a multi-view case over the whole problem and a multi-view case over each instance, each with its own set of desirable properties. In this paper, we extend both approaches to the same problem, where the underlying multi-view case is a case over two distinct views. We provide a formal language for such a task, for which a multi-view model is more than a single view, and show how to construct an improved one from scratch. We provide computational examples of the problem in a dataset of 60,000 patients as well as a benchmark problem with similar sample size using both models. We demonstrate that the proposed language can be very useful for this situation.

Many graph-mining and neural networks have achieved state-of-the-art performance on large scale. This paper presents a framework for graph mining based on neural networks for graphs and shows that it can be used at very high scale as well. It uses the neural network as a representation of graph data and provides a flexible solution to the problem. It aims to predict the future graphs based on the graph’s past patterns and to learn a network from the graph’s history, both from training data and from data from previous studies. It is also shown that a graph data network can be used for a large number of graph prediction tasks.

Training Discriminative Deep Neural Networks with Sparsity-Induced Penalty

Faster Rates for the Regularized Loss Modulation on Continuous Data

On a Generative Baseline for Modeling Clinical Trials

A Generalized K-nearest Neighbour Method for Data Clustering

Recurrent Neural Networks for GraphsMany graph-mining and neural networks have achieved state-of-the-art performance on large scale. This paper presents a framework for graph mining based on neural networks for graphs and shows that it can be used at very high scale as well. It uses the neural network as a representation of graph data and provides a flexible solution to the problem. It aims to predict the future graphs based on the graph’s past patterns and to learn a network from the graph’s history, both from training data and from data from previous studies. It is also shown that a graph data network can be used for a large number of graph prediction tasks.