20 citations · 47 across the 13 of their papers we have counts for
15 papers
Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science
Victoria Preston, Genevieve Flaspohler, Anna P. M. Michel +2
In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uni…
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis
Or Dinari, Raz Zamir, John W. Fisher +1
In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting…
Hierarchical Entity Alignment for Attribute-Rich Event-Driven Graphs
Elizabeth Hou, Joanna Brown, John Fisher
This paper addresses the problem of entity alignment in attribute-rich event-driven graphs. Unlike many other entity alignment problems, we are interested in aligning entities base…
Lightweight Data Fusion with Conjugate Mappings
Christopher L. Dean, Stephen J. Lee, Jason Pacheco +1
We present an approach to data fusion that combines the interpretability of structured probabilistic graphical models with the flexibility of neural networks. The proposed method,…
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking
David S. Hayden, Sue Zheng, John W. Fisher
Robust data association is critical for analysis of long-term motion trajectories in complex scenes. In its absence, trajectory precision suffers due to periods of kinematic ambigu…
Information Planning for Text Data
Vadim Smolyakov
Information planning enables faster learning with fewer training examples. It is particularly applicable when training examples are costly to obtain. This work examines the advanta…
Bayesian Nonparametric Modeling of Driver Behavior using HDP Split-Merge Sampling Algorithm
Vadim Smolyakov, Julian Straub, Sue Zheng +1
Modern vehicles are equipped with increasingly complex sensors. These sensors generate large volumes of data that provide opportunities for modeling and analysis. Here, we are inte…
Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
Vadim Smolyakov, Qiang Liu, John W. Fisher
For large scale on-line inference problems the update strategy is critical for performance. We derive an adaptive scan Gibbs sampler that optimizes the update frequency by selectin…
Direction-Aware Semi-Dense SLAM
Julian Straub, Randi Cabezas, John Leonard +1
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding. In a step towards fully integrated probabilistic geom…
A Nonparametric Model for Multimodal Collaborative Activities Summarization
Guy Rosman, John W. Fisher, Daniela Rus
Ego-centric data streams provide a unique opportunity to reason about joint behavior by pooling data across individuals. This is especially evident in urban environments teeming wi…
Small-Variance Nonparametric Clustering on the Hypersphere
Julian Straub, Trevor Campbell, Jonathan P. How +1
Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer v…
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Julian Straub, Trevor Campbell, Jonathan P. How +1
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to th…
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
Trevor Campbell, Julian Straub, John W. Fisher +1
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models. In the proposed framework, processing nodes rece…
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen +2
Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original…
Efficient Topology-Controlled Sampling of Implicit Shapes
Jason Chang, John W. Fisher
Sampling from distributions of implicitly defined shapes enables analysis of various energy functionals used for image segmentation. Recent work describes a computationally efficie…