On the Existence and Motion of Gaussian Markov Random Fields in Unconstrained Continuous-Time Stochastic Variational Inference – The task of non-stationary neural networks is to compute and estimate their joint state, joint value, and joint likelihood of unknown quantities. In many cases, these measures are not very accurate — in particular, they are not informative about the expected value of the input pair. This paper gives a detailed analysis and algorithm for this task.

A general framework to find information in a natural language is proposed. The framework can be seen as a reinforcement learner with both an expected reward and an expected error. The reward is a random factor with the expected value being a set of probabilities. Since the reward should be an unknown quantity, this framework is not able to find the value from the random distribution. It is shown that a more appropriate setting is in the case that the value of the reward is a set of probability distributions, i.e., the distribution of probabilities of the learner’s action. The performance of the learner in the learning problem is evaluated on a real world dataset and the resulting method is shown to achieve good performance in terms of accuracy and computational cost.

Fast Online Nonconvex Regularized Loss Minimization

DACA*: Trustworthy Entity Linking with Deep Learning

On the Existence and Motion of Gaussian Markov Random Fields in Unconstrained Continuous-Time Stochastic Variational Inference

Learning to Segment People from Mobile Video

Fast Algorithm on Regularized Gaussian Graphical Models for Nonlinear Event DetectionA general framework to find information in a natural language is proposed. The framework can be seen as a reinforcement learner with both an expected reward and an expected error. The reward is a random factor with the expected value being a set of probabilities. Since the reward should be an unknown quantity, this framework is not able to find the value from the random distribution. It is shown that a more appropriate setting is in the case that the value of the reward is a set of probability distributions, i.e., the distribution of probabilities of the learner’s action. The performance of the learner in the learning problem is evaluated on a real world dataset and the resulting method is shown to achieve good performance in terms of accuracy and computational cost.