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.

The authors propose a new method of Convolutional Neural Networks (CNN), which is inspired by the traditional sequential optimization for the multi-subject image classification problem. We propose to use a supervised learning method called Gaussian PDEs (GNNs) to map the image regions to the training set of the CNN method. The neural networks are designed for a particular purpose of the image classification problem. The proposed CNN method is based on the GNN’s feature vector representation, the feature representation of the multi-subject image classification problem, and its optimization task. The GNN model has to represent the data in a sparse space using a Gaussian process prior. This work is also motivated by the data augmentation problem, which is an important data augmentation problem where a large number of images undergo multiple augmentation to obtain a higher classification performance. Experimental results show that the proposed method outperforms the state-of-the-art method, while having a negligible performance degradation of accuracy.

Context-aware Topic Modeling

Image Registration Using Conditional Random Fields with Application to Segmenting Dense Multi-modal Images

Dynamic Systems as a Multi-Agent Simulation

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  • Efficient Learning on a Stochastic Neural Network

    A Random Fourier Transform Approach to Compression for Multi-Subject Clinical Image ClassificationThe authors propose a new method of Convolutional Neural Networks (CNN), which is inspired by the traditional sequential optimization for the multi-subject image classification problem. We propose to use a supervised learning method called Gaussian PDEs (GNNs) to map the image regions to the training set of the CNN method. The neural networks are designed for a particular purpose of the image classification problem. The proposed CNN method is based on the GNN’s feature vector representation, the feature representation of the multi-subject image classification problem, and its optimization task. The GNN model has to represent the data in a sparse space using a Gaussian process prior. This work is also motivated by the data augmentation problem, which is an important data augmentation problem where a large number of images undergo multiple augmentation to obtain a higher classification performance. Experimental results show that the proposed method outperforms the state-of-the-art method, while having a negligible performance degradation of accuracy.


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