Dynamic Systems as a Multi-Agent Simulation


Dynamic Systems as a Multi-Agent Simulation – The recent advances in AI applications have proven to be highly successful. In this paper, we present a system that uses a human-generated video from a mobile phone to perform complex tasks such as action recognition and vision in a robotic arm, as a semi-supervised process. We train the robot to perform multiple, sequential, action-based tasks, based on the action set that human players perform on the video. These tasks are presented as a new feature from the video, which could be used as a proxy to measure cognitive activity. The video captured by the robot shows human players performing multiple actions and actions on different video frames, in order to assess the visual state of the agent. We show how in this way the robotic arm and our video can be integrated to a single, sequential action detection system. In particular, we show how to train an action-tracking system that aims to recognize the actions of each player as a sequence of action clusters. We analyze the results of both the robot and human tasks to demonstrate the effectiveness of the system.

A simple method to compute a low-rank posterior can produce inaccurate results, which may lead to overfitting. In this work we propose a novel strategy to improve the accuracy of the posterior obtained by learning the optimal parameters, which allows us to use the sparse information and the residuals for parameter estimation. Our approach is compared with traditional variational methods for data-dependent feature clustering in three tasks: feature extraction, object classification and object segmentation. Our method achieved state-of-the-art accuracy while being an efficient and compact version of a fully supervised CNN. Moreover, we demonstrate the effectiveness of our approach in generating the most accurate and informative features for many datasets, including CNNs for which the data can be corrupted.

Evaluating the Robustness of Probabilistic Models by Identifying Generalization Bias

Optimal Bounds for Online Convex Optimization Problems via Random Projections

Dynamic Systems as a Multi-Agent Simulation

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  • A Generalized Online Convex Optimization Framework for Stochastic Nonparametric Learning

    Improving the performance of the CNN-based Blood Flow Prediction Model by combining latent classifiersA simple method to compute a low-rank posterior can produce inaccurate results, which may lead to overfitting. In this work we propose a novel strategy to improve the accuracy of the posterior obtained by learning the optimal parameters, which allows us to use the sparse information and the residuals for parameter estimation. Our approach is compared with traditional variational methods for data-dependent feature clustering in three tasks: feature extraction, object classification and object segmentation. Our method achieved state-of-the-art accuracy while being an efficient and compact version of a fully supervised CNN. Moreover, we demonstrate the effectiveness of our approach in generating the most accurate and informative features for many datasets, including CNNs for which the data can be corrupted.


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