An extended Stochastic Block model for learning Bayesian networks from incomplete data – Recent work has shown that deep learning can be used as a platform for learning to predict future events. Despite this, it is still a challenging problem. It is unclear why such a simple yet useful network architecture can be used to achieve this, but there exist a few examples where Bayesian networks have been used in the past. We propose a novel framework to tackle this problem by leveraging the ability of deep architectures to be both modular and modular in order to address the challenges posed by the problem. Furthermore, we present a novel application of our framework for learning Deep Neural Networks from incomplete data.

This paper proposes a new framework for learning collaborative reasoning for the task of recognizing user-provided sentences. The task is to recognize a user-provided sentence and to infer a posterior of the sentence corresponding to the user’s knowledge. We propose two new methods that are equivalent to the prior-based retrieval approaches, with the goal of using this task to identify user-provided sentence and infer its posterior. We define a metric for the predictive ability of the posterior, which we test empirically on a number of annotated examples of user-provided sentences. The proposed algorithm outperforms the previously established retrieval approaches, outperforming both the baselines and the human experts, and the best of the human experts.

Dealing with Difficult Matchings in Hashing

An efficient framework for fuzzy classifiers

An extended Stochastic Block model for learning Bayesian networks from incomplete data

Video Anomaly Detection Using Learned Convnet Features

Classifying User’s Manual based on natural experiments with tagged dataThis paper proposes a new framework for learning collaborative reasoning for the task of recognizing user-provided sentences. The task is to recognize a user-provided sentence and to infer a posterior of the sentence corresponding to the user’s knowledge. We propose two new methods that are equivalent to the prior-based retrieval approaches, with the goal of using this task to identify user-provided sentence and infer its posterior. We define a metric for the predictive ability of the posterior, which we test empirically on a number of annotated examples of user-provided sentences. The proposed algorithm outperforms the previously established retrieval approaches, outperforming both the baselines and the human experts, and the best of the human experts.