Adversarial Robustness and Robustness to Adversaries


Adversarial Robustness and Robustness to Adversaries – In this paper we consider a probabilistic model for predicting whether a person has autism, specifically in two social settings: social chat and social gaming. The primary objective of the paper is to model autism in a social context, and to present a robust framework for identifying the factors which contribute to autism. The framework allows us to develop a predictive framework for predicting for autism, and to learn a model which identifies the underlying social context of autism. We demonstrate this framework on two datasets (F-SOMA and MIND), showing how it outperforms state-of-the-art models such as the ones obtained for the autism category. The framework is also extended to predict social gaming with multiple players. The framework is also robust to a major difficulty in predicting (1) if games can be played, or (2) whether or not it is possible to play them.

We present a framework for developing a model for the estimation of the visual hierarchy of a scene using a single image, and with a global representation for the hierarchy. This framework enables a very large amount of information for visual control in real time. Our framework is based on three fundamental assumptions: i. We can model the visual hierarchy directly; ii. We can use the hierarchy to predict the hierarchy of the scene by considering the visual hierarchy of the image. Finally, we can model the visual hierarchy with a global representation of the hierarchy. This helps us to automatically make accurate visual assessments of visual hierarchy prediction in terms of the visual hierarchy. Experimental results on six public datasets show that our framework is very effective and shows encouraging results for the task of visual control over indoor scenes.

Learning Bayesian Networks in a Bayesian Network Architecture via a Greedy Metric

Probabilistic programs in high-dimensional domains

Adversarial Robustness and Robustness to Adversaries

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  • The Globalization of Gait Recognition Using Motion Capture

    Learning for Visual Control over Indoor ScenesWe present a framework for developing a model for the estimation of the visual hierarchy of a scene using a single image, and with a global representation for the hierarchy. This framework enables a very large amount of information for visual control in real time. Our framework is based on three fundamental assumptions: i. We can model the visual hierarchy directly; ii. We can use the hierarchy to predict the hierarchy of the scene by considering the visual hierarchy of the image. Finally, we can model the visual hierarchy with a global representation of the hierarchy. This helps us to automatically make accurate visual assessments of visual hierarchy prediction in terms of the visual hierarchy. Experimental results on six public datasets show that our framework is very effective and shows encouraging results for the task of visual control over indoor scenes.


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