Publications

Publications
Publications
We strongly believe in open source and giving to our community. We work directly with researchers in academia and seek out new perspectives with our intern and fellowship programs. We generalize our solutions and release them to the world as open source projects. We host discussions and publish our results.

Publications

IEEE Conference on Robotics and Automation , (ICRA01), 2001, Seoul, South Korea

Learning Hierarchical Partially Observable Markov Decision Processes for Robot Navigation

Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan

We propose and investigate a general framework for hierarchical modeling of partially observable environments, such as oce buildings, using Hierarchical Hidden Markov Models (HHMMs). Our main goal is to explore hierarchical modeling as a basis for designing more ecient methods for model construction and useage.

As a case study we focus on indoor robot navigation and show how this framework can be used to learn a hierarchy of models of the environment at dierent levels of spatial abstraction. We introduce the idea of model reuse that can be used to combine already learned models into a larger model.

We describe an extension of the HHMM model to includes actions, which we call hierarchical POMDPs, and describe a modied hierarchical Baum-Welch algorithm to learn these models. We train dierent families of hierarchical models for a simulated and a real world corridor environment and compare them with the standard \at" representation of the same environment.

We show that the hierarchical POMDP approach, combined with model reuse, allows learning hierarchical models that t the data better and train faster than at models.

Keywords
Internet Commerce and Software Agents: Cases, Technologies and Opportunities. Rahman, S.M. and Bignall, R. eds. Idea Group. 2001

Distributed Recommender Systems: New Opportunities for Internet Commerce

Badrul Sarwar, Joseph Konstan, John Riedl, Badrul Sarwar, Joseph Konstan, John Riedl

No Information

Keywords
ICML Workshop on Machine Learning of Spatial Knowledge, July 2, 2000, Stanford University

Learning and Planning with Hierarchical Stochastic Models for Robot Navigation

Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan, Georgios Theocharous, Khashayar Rohanimanesh, Sridhar Mahadevan

We propose and investigate a method for hierarchical learning and planning in partially observable environments using the framework of Hierarchical Hidden Markov Models (HHMMs).

Our main goal is to use hierarchical modeling as a basis for exploring more efficient learning and planning algorithms. As a case study we focus on indoor robot navigation problem and will show how this framework can be used to learn a hierarchy of maps of the environment at different levels of spatial abstraction.

We train different families of HHMMs for a real corridor environment and compare them with the standard HMM representation of the same environment. We find significant bene ts to using HHMMs in terms of the fit of the model to the training data, localization of the robot, and the ability to infer the structure of the environment. We also introduce the idea of model reuse that can be used to combine already learned models into a larger model

Keywords
Differential Integral Equations, 13, pp. 1189-1199. 2000

Self-intersections for the surface diffusion and the volume preserving mean curvature flow

Uwe Mayer, Gieri Simonett

We prove that the surface diffusion flow and the volume preserving mean curvature flow can drive embedded hypersurfaces to self-intersections.

Keywords
Categories
Proceedings of the Seventh International Conference on Evolution Equations: Applications to Physics, Industry, Life Sciences and Economics – EVEQ2000

Self-intersections for the Willmore Flow

Uwe Mayer, Gieri Simonett

We prove that the Willmore flow can drive embedded surfaces to self-intersections in finite time

Keywords
Categories
Nuclear Science Symposium and Medical Imaging Conference, Lyon, France, October 2000

Performance of parametric shape estimators for 2D and 3D imaging systems

Robinson Piramuthu, Alfred O Hero III

Presents Cramer-Rao (CR) bounds on error covariance for 2D and 3D parametric shape estimation. The motivation for this paper is ECT image reconstruction and uptake estimation with side information corresponding to organ boundaries extracted from high resolution MRI or CT.

It is important to understand the fundamental limitations on boundary estimation error covariance so as to gauge the utility of such side information. The authors present asymptotic forms of the Fisher information matrix for estimating 2D and 3D boundaries under a B-spline polar shape parameterization.

They show that circular (2D) and spherical (3D) shapes are the easiest to estimate in the sense of yielding maximum Fisher information. They also study the worst case shapes under near circularity and near sphericity constraints. Finally, a simulation is presented to illustrate the tightness of the CR bound for a simple 3D shape estimator utilizing edge filtering.

Keywords
Categories
International Conference on Knowledge Discovery and Data Mining (KDD-2000), August 2000

Alpha seeding for support vector machines

Dennis DeCoste, K. Wagstaff

A key practical obstacle in applying support vector machines to many large-scale data mining tasks is that SVM’s generally scale quadratically (or worse) in the number of examples or support vectors.

This complexity is further compounded when a specific SVM training is but one of many, such as in Leave-One-Out-Cross-Validation (LOOCV) for determining optimal SVM kernel parameters or as in wrapper-based feature selection. In this paper we explore new techniques for reducing the amortized cost of each such SVM training, by seeding successive SVM trainings with the results of previous similar trainings.

Keywords
Computer Vision and Pattern Recognition (CVPR-2000), June 2000

Distortion-invariant recognition via jittered queries

Dennis DeCoste, M.C. Burl

This paper presents a new approach for achieving distortion-invariant recognition and classification. A test example to be classified is viewed as a query intended to find similar examples in the training set (or class models derived from the training set). The key idea is that instead of querying with a single pattern, we construct a more robust query, based on the family of patterns formed by distorting the test example.

Although query execution is slower than if the invariances were successfully pre-compiled during training, there are significant advantages in several contexts:

(i) providing invariances in memory-based learning,

(ii) in model selection, where reducing training time at the expense of test time is a desirable trade-off, and

(iii) in enabling robust, ad hoc searches based on a single example. Preliminary tests for memory-based learning on the NIST handwritten digit database with a limited set of shearing and translation distortions produced an error rate of 1.35%.

Keywords

Pages