8,173 research outputs found
Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels
We present a Gaussian kernel loss function and training algorithm for
convolutional neural networks that can be directly applied to both distance
metric learning and image classification problems. Our method treats all
training features from a deep neural network as Gaussian kernel centres and
computes loss by summing the influence of a feature's nearby centres in the
feature embedding space. Our approach is made scalable by treating it as an
approximate nearest neighbour search problem. We show how to make end-to-end
learning feasible, resulting in a well formed embedding space, in which
semantically related instances are likely to be located near one another,
regardless of whether or not the network was trained on those classes. Our
approach outperforms state-of-the-art deep metric learning approaches on
embedding learning challenges, as well as conventional softmax classification
on several datasets.Comment: Accepted in the International Conference on Image Processing (ICIP)
2018. Formerly titled Nearest Neighbour Radial Basis Function Solvers for
Deep Neural Network
Visualising the structure of document search results: A comparison of graph theoretic approaches
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion
A Parallel Adaptive P3M code with Hierarchical Particle Reordering
We discuss the design and implementation of HYDRA_OMP a parallel
implementation of the Smoothed Particle Hydrodynamics-Adaptive P3M (SPH-AP3M)
code HYDRA. The code is designed primarily for conducting cosmological
hydrodynamic simulations and is written in Fortran77+OpenMP. A number of
optimizations for RISC processors and SMP-NUMA architectures have been
implemented, the most important optimization being hierarchical reordering of
particles within chaining cells, which greatly improves data locality thereby
removing the cache misses typically associated with linked lists. Parallel
scaling is good, with a minimum parallel scaling of 73% achieved on 32 nodes
for a variety of modern SMP architectures. We give performance data in terms of
the number of particle updates per second, which is a more useful performance
metric than raw MFlops. A basic version of the code will be made available to
the community in the near future.Comment: 34 pages, 12 figures, accepted for publication in Computer Physics
Communication
Implementation for spatial data of the shared nearest neighbour with metric data structures
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Engenharia Informátic
Small-world networks, distributed hash tables and the e-resource discovery problem
Resource discovery is one of the most important underpinning problems behind producing a scalable,
robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery
and management problem have been made in various computational grid environments and prototypes
over the last decade. Computational resources and services in modern grid and cloud environments can be
modelled as an overlay network superposed on the physical network structure of the Internet and World
Wide Web. We discuss some of the main approaches to resource discovery in the context of the general
properties of such an overlay network. We present some performance data and predicted properties based
on algorithmic approaches such as distributed hash table resource discovery and management. We describe
a prototype system and use its model to explore some of the known key graph aspects of the global
resource overlay network - including small-world and scale-free properties
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