Distributed Estimation with Information-Seeking Control in Agent Network

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.Comment: 17 pages, 10 figure

Entropy coding for training deep belief networks with imbalanced and unlabeled data

Session 1aSCb - Speech Communication: Speech Processing Potpourri (Poster Session): no. 1aSCb1Training deep belief networks (DBNs) is normally done with large data sets. In this work, the goal is to predict traces of the surface of the tongue in ultrasoundimages of the mouth during speech. Performance on this task can be dramatically enhanced by pre-training a DBN jointly on human-supplied traces and ultrasoundimages, then training a modified version of the network to predict traces from ultrasound only. However, hand-tracing the entire dataset of ultrasoundimages is extremely labor intensive. Moreover, the dataset is highly imbalanced since many images are extremely similar. This work presents a bootstrapping method which takes advantage of this imbalance, iteratively selecting a small subset of images to be hand-traced, then (re)training the DBN, making use of an entropy-based diversity measure for the initial selection. With this approach, a three-fold reduction in human time required to trace an entire dataset with human-level accuracy was achieved.published_or_final_versio