Towards a Deep Multitask Understanding of Task Dynamics – Anomaly detection provides a means for automatic and interpretable diagnosis of real-world events. We consider the problem of detecting anomalous systems in two aspects: (1) detecting the presence of anomalous devices and (2) detecting the presence of anomalous systems. Our approach proposes to first detect and assess any anomalous system and then apply a predictive model to infer the anomaly. Based on our proposed approach, we identify anomaly systems as well as a system that is the result of the system anomalous. To evaluate the predictive models, we develop a method of combining the predictive models with the hypothesis testing model and create a new anomaly detection model for each problem that is capable of detecting or recognizing anomalous systems with high probability. The method has been applied to a series of real-world datasets for which it showed similar or higher accuracy than the state-of-the-art methods.

We present an automated strategy for a new game, where you are the main character in a campaign of a human-robot team. We show that the system, named AIXG, is capable of predicting the outcome of the campaign, and that it can be used to help humans in the campaign in a very powerful way. Our system is based on an optimization algorithm based on the minimax method for the cost function and an online version of the max-product strategy which was used to improve the minimax and max-product strategies. We show that in some situations our algorithm can be more effective than the minimax method and is much more powerful than max-product.

Multispectral Image Fusion using Conditional Density Estimation

Optimal Riemannian transport for sparse representation: A heuristic scheme

Towards a Deep Multitask Understanding of Task Dynamics

Semi-Supervised Clustering-Based Clustering Algorithm using Convex Relaxation

ProEval: A Risk-Agnostic Decision Support SystemWe present an automated strategy for a new game, where you are the main character in a campaign of a human-robot team. We show that the system, named AIXG, is capable of predicting the outcome of the campaign, and that it can be used to help humans in the campaign in a very powerful way. Our system is based on an optimization algorithm based on the minimax method for the cost function and an online version of the max-product strategy which was used to improve the minimax and max-product strategies. We show that in some situations our algorithm can be more effective than the minimax method and is much more powerful than max-product.