19,156 research outputs found
Hierarchical mutual information for the comparison of hierarchical community structures in complex networks
The quest for a quantitative characterization of community and modular
structure of complex networks produced a variety of methods and algorithms to
classify different networks. However, it is not clear if such methods provide
consistent, robust and meaningful results when considering hierarchies as a
whole. Part of the problem is the lack of a similarity measure for the
comparison of hierarchical community structures. In this work we give a
contribution by introducing the {\it hierarchical mutual information}, which is
a generalization of the traditional mutual information, and allows to compare
hierarchical partitions and hierarchical community structures. The {\it
normalized} version of the hierarchical mutual information should behave
analogously to the traditional normalized mutual information. Here, the correct
behavior of the hierarchical mutual information is corroborated on an extensive
battery of numerical experiments. The experiments are performed on artificial
hierarchies, and on the hierarchical community structure of artificial and
empirical networks. Furthermore, the experiments illustrate some of the
practical applications of the hierarchical mutual information. Namely, the
comparison of different community detection methods, and the study of the
consistency, robustness and temporal evolution of the hierarchical modular
structure of networks.Comment: 14 pages and 12 figure
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.Comment: 9 pages, 5 figures, 3 table
High-dimensional approximate nearest neighbor: k-d Generalized Randomized Forests
We propose a new data-structure, the generalized randomized kd forest, or
kgeraf, for approximate nearest neighbor searching in high dimensions. In
particular, we introduce new randomization techniques to specify a set of
independently constructed trees where search is performed simultaneously, hence
increasing accuracy. We omit backtracking, and we optimize distance
computations, thus accelerating queries. We release public domain software
geraf and we compare it to existing implementations of state-of-the-art methods
including BBD-trees, Locality Sensitive Hashing, randomized kd forests, and
product quantization. Experimental results indicate that our method would be
the method of choice in dimensions around 1,000, and probably up to 10,000, and
pointsets of cardinality up to a few hundred thousands or even one million;
this range of inputs is encountered in many critical applications today. For
instance, we handle a real dataset of images represented in 960
dimensions with a query time of less than sec on average and 90\% responses
being true nearest neighbors
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