Robot Learning with a Spatial, Temporal, and Causal And-Or Graph Caiming Xiong∗ , Nishant Shukla∗ , Wenlong Xiong, and Song-Chun Zhu Abstract— We propose a stochastic graph-based framework for a robot to understand

First Page		Document Content
Date: 2017-05-18 12:57:06 Metaphysics Mathematics Applied mathematics Computational neuroscience Graph theory Artificial neural network Market research Mathematical psychology Bayesian network Causality Graph rewriting Motion planning		Robot Learning with a Spatial, Temporal, and Causal And-Or Graph Caiming Xiong∗ , Nishant Shukla∗ , Wenlong Xiong, and Song-Chun Zhu Abstract— We propose a stochastic graph-based framework for a robot to understand Add to Reading List Source URL: shukla.io Download Document from Source Website File Size: 1,27 MB Share Document on Facebook

	SCAN 2018 Post-conference Proceedings Special Issue of Journal of Computational and Applied Mathematics Call for Papers Special Issue on the 18th International Symposium on Scientific Computing, Computer Arithmetic, DocID: 1xVSx - View Document
	Performance Evaluation and Optimization Models for Processing Networks with Queue-Dependent Production Quantities by John S. Hollywood S.B. Applied Mathematics DocID: 1xVdz - View Document
	Mixture Density Networks Christopher M. Bishop Neural Computing Research Group Dept. of Computer Science and Applied Mathematics Aston University DocID: 1xUJf - View Document
	Mathematics_BS_Applied.pdf DocID: 1xTYO - View Document
	A BOOTSTRAP INTERVAL ESTIMATOR FOR BAYES’ CLASSIFICATION ERROR Chad M. Hawes and Carey E. Priebe Johns Hopkins University Department of Applied Mathematics and Statistics Baltimore, MDABSTRACT DocID: 1vrMQ - View Document