2,621 research outputs found
OTW: Optimal Transport Warping for Time Series
Dynamic Time Warping (DTW) has become the pragmatic choice for measuring
distance between time series. However, it suffers from unavoidable quadratic
time complexity when the optimal alignment matrix needs to be computed exactly.
This hinders its use in deep learning architectures, where layers involving DTW
computations cause severe bottlenecks. To alleviate these issues, we introduce
a new metric for time series data based on the Optimal Transport (OT)
framework, called Optimal Transport Warping (OTW). OTW enjoys linear time/space
complexity, is differentiable and can be parallelized. OTW enjoys a moderate
sensitivity to time and shape distortions, making it ideal for time series. We
show the efficacy and efficiency of OTW on 1-Nearest Neighbor Classification
and Hierarchical Clustering, as well as in the case of using OTW instead of DTW
in Deep Learning architectures.Comment: This is an extended version of an ICASSP 2023 accepted paper
https://ieeexplore.ieee.org/document/1009591
Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
Many graphics and vision problems can be expressed as non-linear least
squares optimizations of objective functions over visual data, such as images
and meshes. The mathematical descriptions of these functions are extremely
concise, but their implementation in real code is tedious, especially when
optimized for real-time performance on modern GPUs in interactive applications.
In this work, we propose a new language, Opt (available under
http://optlang.org), for writing these objective functions over image- or
graph-structured unknowns concisely and at a high level. Our compiler
automatically transforms these specifications into state-of-the-art GPU solvers
based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate
different variations of the solver, so users can easily explore tradeoffs in
numerical precision, matrix-free methods, and solver approaches. In our
results, we implement a variety of real-world graphics and vision applications.
Their energy functions are expressible in tens of lines of code, and produce
highly-optimized GPU solver implementations. These solver have performance
competitive with the best published hand-tuned, application-specific GPU
solvers, and orders of magnitude beyond a general-purpose auto-generated
solver
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