306 research outputs found
Random projections for high-dimensional curves
Modern time series analysis requires the ability to handle datasets that are
inherently high-dimensional; examples include applications in climatology,
where measurements from numerous sensors must be taken into account, or
inventory tracking of large shops, where the dimension is defined by the number
of tracked items. The standard way to mitigate computational issues arising
from the high-dimensionality of the data is by applying some dimension
reduction technique that preserves the structural properties of the ambient
space. The dissimilarity between two time series is often measured by
``discrete'' notions of distance, e.g. the dynamic time warping, or the
discrete Fr\'echet distance, or simply the Euclidean distance. Since all these
distance functions are computed directly on the points of a time series, they
are sensitive to different sampling rates or gaps. The continuous Fr\'echet
distance offers a popular alternative which aims to alleviate this by taking
into account all points on the polygonal curve obtained by linearly
interpolating between any two consecutive points in a sequence.
We study the ability of random projections \`a la Johnson and Lindenstrauss
to preserve the continuous Fr\'echet distance of polygonal curves by
effectively reducing the dimension. In particular, we show that one can reduce
the dimension to , where is the total number of
input points while preserving the continuous Fr\'echet distance between any two
determined polygonal curves within a factor of . We conclude
with applications on clustering.Comment: 22 page
Distance Measures for Embedded Graphs
We introduce new distance measures for comparing straight-line embedded
graphs based on the Fr\'echet distance and the weak Fr\'echet distance. These
graph distances are defined using continuous mappings and thus take the
combinatorial structure as well as the geometric embeddings of the graphs into
account. We present a general algorithmic approach for computing these graph
distances. Although we show that deciding the distances is NP-hard for general
embedded graphs, we prove that our approach yields polynomial time algorithms
if the graphs are trees, and for the distance based on the weak Fr\'echet
distance if the graphs are planar embedded. Moreover, we prove that deciding
the distances based on the Fr\'echet distance remains NP-hard for planar
embedded graphs and show how our general algorithmic approach yields an
exponential time algorithm and a polynomial time approximation algorithm for
this case.Comment: 27 pages, 14 Figure
Continuous Hierarchical Representations with Poincar\'e Variational Auto-Encoders
The variational auto-encoder (VAE) is a popular method for learning a
generative model and embeddings of the data. Many real datasets are
hierarchically structured. However, traditional VAEs map data in a Euclidean
latent space which cannot efficiently embed tree-like structures. Hyperbolic
spaces with negative curvature can. We therefore endow VAEs with a Poincar\'e
ball model of hyperbolic geometry as a latent space and rigorously derive the
necessary methods to work with two main Gaussian generalisations on that space.
We empirically show better generalisation to unseen data than the Euclidean
counterpart, and can qualitatively and quantitatively better recover
hierarchical structures.Comment: Advances in Neural Information Processing System
Multimodal Controller for Generative Models
Class-conditional generative models are crucial tools for data generation
from user-specified class labels. Existing approaches for class-conditional
generative models require nontrivial modifications of backbone generative
architectures to model conditional information fed into the model. This paper
introduces a plug-and-play module named `multimodal controller' to generate
multimodal data without introducing additional learning parameters. In the
absence of the controllers, our model reduces to non-conditional generative
models. We test the efficacy of multimodal controllers on CIFAR10, COIL100, and
Omniglot benchmark datasets. We demonstrate that multimodal controlled
generative models (including VAE, PixelCNN, Glow, and GAN) can generate
class-conditional images of significantly better quality when compared with
conditional generative models. Moreover, we show that multimodal controlled
models can also create novel modalities of images
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
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