38,061 research outputs found
Patterns of Scalable Bayesian Inference
Datasets are growing not just in size but in complexity, creating a demand
for rich models and quantification of uncertainty. Bayesian methods are an
excellent fit for this demand, but scaling Bayesian inference is a challenge.
In response to this challenge, there has been considerable recent work based on
varying assumptions about model structure, underlying computational resources,
and the importance of asymptotic correctness. As a result, there is a zoo of
ideas with few clear overarching principles.
In this paper, we seek to identify unifying principles, patterns, and
intuitions for scaling Bayesian inference. We review existing work on utilizing
modern computing resources with both MCMC and variational approximation
techniques. From this taxonomy of ideas, we characterize the general principles
that have proven successful for designing scalable inference procedures and
comment on the path forward
KVN: Keypoints Voting Network with Differentiable RANSAC for Stereo Pose Estimation
Object pose estimation is a fundamental computer vision task exploited in
several robotics and augmented reality applications. Many established
approaches rely on predicting 2D-3D keypoint correspondences using RANSAC
(Random sample consensus) and estimating the object pose using the PnP
(Perspective-n-Point) algorithm. Being RANSAC non-differentiable,
correspondences cannot be directly learned in an end-to-end fashion. In this
paper, we address the stereo image-based object pose estimation problem by (i)
introducing a differentiable RANSAC layer into a well-known monocular pose
estimation network; (ii) exploiting an uncertainty-driven multi-view PnP solver
which can fuse information from multiple views. We evaluate our approach on a
challenging public stereo object pose estimation dataset, yielding
state-of-the-art results against other recent approaches. Furthermore, in our
ablation study, we show that the differentiable RANSAC layer plays a
significant role in the accuracy of the proposed method. We release with this
paper the open-source implementation of our method.Comment: Submitted to IEEE Robotics and Automation Letter
Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem
We define and analyze a multi-agent multi-armed bandit problem in which
decision-making agents can observe the choices and rewards of their neighbors.
Neighbors are defined by a network graph with heterogeneous and stochastic
interconnections. These interactions are determined by the sociability of each
agent, which corresponds to the probability that the agent observes its
neighbors. We design an algorithm for each agent to maximize its own expected
cumulative reward and prove performance bounds that depend on the sociability
of the agents and the network structure. We use the bounds to predict the rank
ordering of agents according to their performance and verify the accuracy
analytically and computationally
Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guide the camera pose optimization. However, these image features are only
sampled randomly in the images, without considering the spatial structures or
geometric information, leading to large errors or failure cases with the
existence of poorly textured areas or in motion blur. Line segment features are
more robust in these environments. In this work, we propose to jointly exploit
points and lines within the framework of uncertainty driven regression forests.
The proposed approach is thoroughly evaluated on three publicly available
datasets against several strong state-of-the-art baselines in terms of several
different error metrics. Experimental results prove the efficacy of our method,
showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Bookmaker Consensus and Agreement for the UEFA Champions League 2008/09
Bookmakers odds are an easily available source of ``prospective" information that is thus often employed for forecasting the outcome of sports events. To investigate the statistical properties of bookmakers odds from a variety of bookmakers for a number of different potential outcomes of a sports event, a class of mixed-effects models is explored, providing information about both consensus and (dis)agreement across bookmakers. In an empirical study for the UEFA Champions League, the most prestigious football club competition in Europe, model selection yields a simple and intuitive model with team-specific means for capturing consensus and team-specific standard deviations reflecting agreement across bookmakers. The resulting consensus forecast performs well in practice, exhibiting high correlation with the actual tournament outcome. Furthermore, the teams' agreement can be shown to be strongly correlated with the predicted consensus and can thus be incorporated in a more parsimonious model for agreement while preserving the same consensus fit.Series: Research Report Series / Department of Statistics and Mathematic
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