15,776 research outputs found
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Efficient Optimization of Echo State Networks for Time Series Datasets
Echo State Networks (ESNs) are recurrent neural networks that only train
their output layer, thereby precluding the need to backpropagate gradients
through time, which leads to significant computational gains. Nevertheless, a
common issue in ESNs is determining its hyperparameters, which are crucial in
instantiating a well performing reservoir, but are often set manually or using
heuristics. In this work we optimize the ESN hyperparameters using Bayesian
optimization which, given a limited budget of function evaluations, outperforms
a grid search strategy. In the context of large volumes of time series data,
such as light curves in the field of astronomy, we can further reduce the
optimization cost of ESNs. In particular, we wish to avoid tuning
hyperparameters per individual time series as this is costly; instead, we want
to find ESNs with hyperparameters that perform well not just on individual time
series but rather on groups of similar time series without sacrificing
predictive performance significantly. This naturally leads to a notion of
clusters, where each cluster is represented by an ESN tuned to model a group of
time series of similar temporal behavior. We demonstrate this approach both on
synthetic datasets and real world light curves from the MACHO survey. We show
that our approach results in a significant reduction in the number of ESN
models required to model a whole dataset, while retaining predictive
performance for the series in each cluster
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