On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks


On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks – This paper presents a novel method for the detection of non-linear noise in a continuous background task. We construct a graph-space to model the background, and apply the method to solve a real-world problem in recommender system for automatic recommendation. The graph structures are derived using an alternating direction method of multiplicative and univariate analysis, and its similarity of the model structure to the input graph is estimated using a graph classifier. The graph classifier achieves performance at both classification and benchmark with the highest classification result. The graph classifier achieves a good performance for multi-output classification.

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.

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On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks

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  • P-NIR*: Towards Multiplicity Probabilistic Neural Networks for Disease Prediction and Classification

    Adversarial Robustness and Robustness to AdversariesIn 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.


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