92 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
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
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
Optimization and Analysis of Distributed Averaging with Short Node Memory
In this paper, we demonstrate, both theoretically and by numerical examples,
that adding a local prediction component to the update rule can significantly
improve the convergence rate of distributed averaging algorithms. We focus on
the case where the local predictor is a linear combination of the node's two
previous values (i.e., two memory taps), and our update rule computes a
combination of the predictor and the usual weighted linear combination of
values received from neighbouring nodes. We derive the optimal mixing parameter
for combining the predictor with the neighbors' values, and carry out a
theoretical analysis of the improvement in convergence rate that can be
obtained using this acceleration methodology. For a chain topology on n nodes,
this leads to a factor of n improvement over the one-step algorithm, and for a
two-dimensional grid, our approach achieves a factor of n^1/2 improvement, in
terms of the number of iterations required to reach a prescribed level of
accuracy
Meta-learning framework with applications to zero-shot time-series forecasting
Can meta-learning discover generic ways of processing time series (TS) from a
diverse dataset so as to greatly improve generalization on new TS coming from
different datasets? This work provides positive evidence to this using a broad
meta-learning framework which we show subsumes many existing meta-learning
algorithms. Our theoretical analysis suggests that residual connections act as
a meta-learning adaptation mechanism, generating a subset of task-specific
parameters based on a given TS input, thus gradually expanding the expressive
power of the architecture on-the-fly. The same mechanism is shown via
linearization analysis to have the interpretation of a sequential update of the
final linear layer. Our empirical results on a wide range of data emphasize the
importance of the identified meta-learning mechanisms for successful zero-shot
univariate forecasting, suggesting that it is viable to train a neural network
on a source TS dataset and deploy it on a different target TS dataset without
retraining, resulting in performance that is at least as good as that of
state-of-practice univariate forecasting models
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