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Found 23 papers, 2 papers with code
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis
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.
Sequential Bayesian Experimental Design with Variable Cost Structure
As a result, many algorithms utilize MI bounds as proxies that lack regret-style guarantees.
Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information
This work introduces macro-action discovery using value-of-information (VoI) for robust and efficient planning in partially observable Markov decision processes (POMDPs).
Lightweight Data Fusion with Conjugate Mappings
We present an approach to data fusion that combines the interpretability of structured probabilistic graphical models with the flexibility of neural networks.
Efficient Data Association and Uncertainty Quantification for Multi-Object Tracking
Robust data association is critical for analysis of long-term motion trajectories in complex scenes.
Adaptive Scan Gibbs Sampler for Large Scale Inference Problems
For large scale on-line inference problems the update strategy is critical for performance.
Bayesian Nonparametric Modeling of Driver Behavior using HDP Split-Merge Sampling Algorithm
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.
Direction-Aware Semi-Dense SLAM
To aide simultaneous localization and mapping (SLAM), future perception systems will incorporate forms of scene understanding.
A Nonparametric Model for Multimodal Collaborative Activities Summarization
We demonstrate the utility of this model for inference tasks such as activity detection, classification, and summarization.
Small-Variance Nonparametric Clustering on the Hypersphere
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.
Information-Driven Adaptive Structured-Light Scanners
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.
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.
Probabilistic Variational Bounds for Graphical Models
We propose a simple Monte Carlo based inference method that augments convex variational bounds by adding importance sampling (IS).
Semantically-Aware Aerial Reconstruction From Multi-Modal Data
We consider a methodology for integrating multiple sensors along with semantic information to enhance scene representations.
Highly-Expressive Spaces of Well-Behaved Transformations: Keeping It Simple
We propose novel finite-dimensional spaces of R - R transformations, n [?]
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models.
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
We then learn a class-specific probabilistic generative models of the transformations in a Riemannian submanifold of the Lie group of diffeomorphisms.
Coresets for k-Segmentation of Streaming Data
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.
Parallel Sampling of HDPs using Sub-Cluster Splits
We develop a sampling technique for Hierarchical Dirichlet process models.
Aerial Reconstructions via Probabilistic Data Fusion
We propose an integrated probabilistic model for multi-modal fusion of aerial imagery, LiDAR data, and (optional) GPS measurements.
A Mixture of Manhattan Frames: Beyond the Manhattan World
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.
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits
We present a novel MCMC sampler for Dirichlet process mixture models that can be used for conjugate or non-conjugate prior distributions.
A Video Representation Using Temporal Superpixels
We develop a generative probabilistic model for temporally consistent superpixels in video sequences.
