2,133 research outputs found
Analysis of error propagation in particle filters with approximation
This paper examines the impact of approximation steps that become necessary
when particle filters are implemented on resource-constrained platforms. We
consider particle filters that perform intermittent approximation, either by
subsampling the particles or by generating a parametric approximation. For such
algorithms, we derive time-uniform bounds on the weak-sense error and
present associated exponential inequalities. We motivate the theoretical
analysis by considering the leader node particle filter and present numerical
experiments exploring its performance and the relationship to the error bounds.Comment: Published in at http://dx.doi.org/10.1214/11-AAP760 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Efficient delay-tolerant particle filtering
This paper proposes a novel framework for delay-tolerant particle filtering
that is computationally efficient and has limited memory requirements. Within
this framework the informativeness of a delayed (out-of-sequence) measurement
(OOSM) is estimated using a lightweight procedure and uninformative
measurements are immediately discarded. The framework requires the
identification of a threshold that separates informative from uninformative;
this threshold selection task is formulated as a constrained optimization
problem, where the goal is to minimize tracking error whilst controlling the
computational requirements. We develop an algorithm that provides an
approximate solution for the optimization problem. Simulation experiments
provide an example where the proposed framework processes less than 40% of all
OOSMs with only a small reduction in tracking accuracy
3D Human Pose and Shape Estimation via HybrIK-Transformer
HybrIK relies on a combination of analytical inverse kinematics and deep
learning to produce more accurate 3D pose estimation from 2D monocular images.
HybrIK has three major components: (1) pretrained convolution backbone, (2)
deconvolution to lift 3D pose from 2D convolution features, (3) analytical
inverse kinematics pass correcting deep learning prediction using learned
distribution of plausible twist and swing angles. In this paper we propose an
enhancement of the 2D to 3D lifting module, replacing deconvolution with
Transformer, resulting in accuracy and computational efficiency improvement
relative to the original HybrIK method. We demonstrate our results on commonly
used H36M, PW3D, COCO and HP3D datasets. Our code is publicly available
https://github.com/boreshkinai/hybrik-transformer
Greedy Gossip with Eavesdropping
This paper presents greedy gossip with eavesdropping (GGE), a novel
randomized gossip algorithm for distributed computation of the average
consensus problem. In gossip algorithms, nodes in the network randomly
communicate with their neighbors and exchange information iteratively. The
algorithms are simple and decentralized, making them attractive for wireless
network applications. In general, gossip algorithms are robust to unreliable
wireless conditions and time varying network topologies. In this paper we
introduce GGE and demonstrate that greedy updates lead to rapid convergence. We
do not require nodes to have any location information. Instead, greedy updates
are made possible by exploiting the broadcast nature of wireless
communications. During the operation of GGE, when a node decides to gossip,
instead of choosing one of its neighbors at random, it makes a greedy
selection, choosing the node which has the value most different from its own.
In order to make this selection, nodes need to know their neighbors' values.
Therefore, we assume that all transmissions are wireless broadcasts and nodes
keep track of their neighbors' values by eavesdropping on their communications.
We show that the convergence of GGE is guaranteed for connected network
topologies. We also study the rates of convergence and illustrate, through
theoretical bounds and numerical simulations, that GGE consistently outperforms
randomized gossip and performs comparably to geographic gossip on
moderate-sized random geometric graph topologies.Comment: 25 pages, 7 figure
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