2,791 research outputs found
Efficient learning of neighbor representations for boundary trees and forests
We introduce a semiparametric approach to neighbor-based classification. We
build off the recently proposed Boundary Trees algorithm by Mathy et al.(2015)
which enables fast neighbor-based classification, regression and retrieval in
large datasets. While boundary trees use an Euclidean measure of similarity,
the Differentiable Boundary Tree algorithm by Zoran et al.(2017) was introduced
to learn low-dimensional representations of complex input data, on which
semantic similarity can be calculated to train boundary trees. As is pointed
out by its authors, the differentiable boundary tree approach contains a few
limitations that prevents it from scaling to large datasets. In this paper, we
introduce Differentiable Boundary Sets, an algorithm that overcomes the
computational issues of the differentiable boundary tree scheme and also
improves its classification accuracy and data representability. Our algorithm
is efficiently implementable with existing tools and offers a significant
reduction in training time. We test and compare the algorithms on the well
known MNIST handwritten digits dataset and the newer Fashion-MNIST dataset by
Xiao et al.(2017).Comment: 9 pages, 2 figure
An economic review of the bitcoin production market and resulting externalities
This paper reviews the existing economic theory on Bitcoin (BTC) production,
analyzes the Bitcoin production market – as well as the associated externalities of
the Bitcoin production process. It discusses how the underlying incentive
mechanism further a competitive arms race that not only contradicts the
philosophy of the underlying consensus scheme but results in an artificially high
level of production demand that imposes significant damages onto society
Antipodally invariant metrics for fast regression-based super-resolution
Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.Peer ReviewedPostprint (author's final draft
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