2 code implementations • 19 Apr 2022 • Or Dinari, Raz Zamir, John W. Fisher III, Oren Freifeld
While Chang and Fisher III's implementation (written in MATLAB/C++) used only CPU and was designed for a single multi-core machine, the packages we proposed here distribute the computations efficiently across either multiple multi-core machines or across mutiple GPU streams.
no code implementations • NeurIPS 2020 • Genevieve Flaspohler, Nicholas A. Roy, John W. Fisher III
This work introduces macro-action discovery using value-of-information (VoI) for robust and efficient planning in partially observable Markov decision processes (POMDPs).
no code implementations • NeurIPS 2020 • Sue Zheng, David Hayden, Jason Pacheco, John W. Fisher III
As a result, many algorithms utilize MI bounds as proxies that lack regret-style guarantees.
no code implementations • 20 Nov 2020 • Christopher L. Dean, Stephen J. Lee, Jason Pacheco, John W. Fisher III
We present an approach to data fusion that combines the interpretability of structured probabilistic graphical models with the flexibility of neural networks.
no code implementations • 13 Nov 2020 • David S. Hayden, Sue Zheng, John W. Fisher III
Robust data association is critical for analysis of long-term motion trajectories in complex scenes.
no code implementations • 27 Jan 2018 • Vadim Smolyakov, Julian Straub, Sue Zheng, John W. Fisher III
In a novel manner, we demonstrate how the sparsity of the personal road network of a driver in conjunction with a hierarchical topic model allows data driven predictions about destinations as well as likely road conditions.
no code implementations • 27 Jan 2018 • Vadim Smolyakov, Qiang Liu, John W. Fisher III
For large scale on-line inference problems the update strategy is critical for performance.
no code implementations • 18 Sep 2017 • Julian Straub, Randi Cabezas, John Leonard, John W. Fisher III
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding.
no code implementations • 4 Sep 2017 • Guy Rosman, John W. Fisher III, Daniela Rus
We demonstrate the utility of this model for inference tasks such as activity detection, classification, and summarization.
no code implementations • CVPR 2015 • Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals.
no code implementations • CVPR 2016 • Guy Rosman, Daniela Rus, John W. Fisher III
We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion.
no code implementations • CVPR 2017 • Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.
no code implementations • NeurIPS 2015 • Qiang Liu, John W. Fisher III, Alexander T. Ihler
We propose a simple Monte Carlo based inference method that augments convex variational bounds by adding importance sampling (IS).
1 code implementation • ICCV 2015 • Oren Freifeld, Soren Hauberg, Kayhan Batmanghelich, John W. Fisher III
We propose novel finite-dimensional spaces of R - R transformations, n [?]
no code implementations • ICCV 2015 • Randi Cabezas, Julian Straub, John W. Fisher III
We consider a methodology for integrating multiple sensors along with semantic information to enhance scene representations.
no code implementations • NeurIPS 2015 • Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models.
no code implementations • 9 Oct 2015 • Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen
We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms.
no code implementations • NeurIPS 2014 • Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
We consider the problem of computing optimal segmentation of such signals by k-piecewise linear function, using only one pass over the data by maintaining a coreset for the signal.
no code implementations • NeurIPS 2014 • Jason Chang, John W. Fisher III
We develop a sampling technique for Hierarchical Dirichlet process models.
no code implementations • CVPR 2014 • Julian Straub, Guy Rosman, Oren Freifeld, John J. Leonard, John W. Fisher III
Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system.
no code implementations • CVPR 2014 • Randi Cabezas, Oren Freifeld, Guy Rosman, John W. Fisher III
We propose an integrated probabilistic model for multi-modal fusion of aerial imagery, LiDAR data, and (optional) GPS measurements.
no code implementations • NeurIPS 2013 • Jason Chang, John W. Fisher III
We present a novel MCMC sampler for Dirichlet process mixture models that can be used for conjugate or non-conjugate prior distributions.
no code implementations • CVPR 2013 • Jason Chang, Donglai Wei, John W. Fisher III
We develop a generative probabilistic model for temporally consistent superpixels in video sequences.