73,448 research outputs found
Efficient Testing without Efficient Regularity
The regularity lemma of Szemeredi turned out to be the most powerful tool for studying the testability of graph properties in the dense graph model. In fact, as we argue in this paper, this lemma can be used in order to prove (essentially) all the previous results in this area. More precisely, a barrier for obtaining an efficient testing algorithm for a graph property P was having an efficient regularity lemma for graphs satisfying P. The problem is that for many natural graph properties (e.g. triangle freeness) it is known that a graph can satisfy P and still only have regular partitions of tower-type size. This means that there was no viable path for obtaining reasonable bounds on the query complexity of testing such properties.
In this paper we consider the property of being induced C_4-free, which also suffers from the fact that a graph might satisfy this property but still have only regular partitions of tower-type size.
By developing a new approach for this problem we manage to overcome this barrier and thus obtain a merely exponential bound for testing this property. This is the first substantial progress on a problem raised by Alon in 2001, and more recently by Alon, Conlon and Fox.
We thus obtain the first example of an efficient testing algorithm that cannot be derived from an efficient version of the regularity lemma
Regular and almost universal hashing: an efficient implementation
Random hashing can provide guarantees regarding the performance of data
structures such as hash tables---even in an adversarial setting. Many existing
families of hash functions are universal: given two data objects, the
probability that they have the same hash value is low given that we pick hash
functions at random. However, universality fails to ensure that all hash
functions are well behaved. We further require regularity: when picking data
objects at random they should have a low probability of having the same hash
value, for any fixed hash function. We present the efficient implementation of
a family of non-cryptographic hash functions (PM+) offering good running times,
good memory usage as well as distinguishing theoretical guarantees: almost
universality and component-wise regularity. On a variety of platforms, our
implementations are comparable to the state of the art in performance. On
recent Intel processors, PM+ achieves a speed of 4.7 bytes per cycle for 32-bit
outputs and 3.3 bytes per cycle for 64-bit outputs. We review vectorization
through SIMD instructions (e.g., AVX2) and optimizations for superscalar
execution.Comment: accepted for publication in Software: Practice and Experience in
September 201
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
Efficient estimation of Banach parameters in semiparametric models
Consider a semiparametric model with a Euclidean parameter and an
infinite-dimensional parameter, to be called a Banach parameter. Assume: (a)
There exists an efficient estimator of the Euclidean parameter. (b) When the
value of the Euclidean parameter is known, there exists an estimator of the
Banach parameter, which depends on this value and is efficient within this
restricted model. Substituting the efficient estimator of the Euclidean
parameter for the value of this parameter in the estimator of the Banach
parameter, one obtains an efficient estimator of the Banach parameter for the
full semiparametric model with the Euclidean parameter unknown. This hereditary
property of efficiency completes estimation in semiparametric models in which
the Euclidean parameter has been estimated efficiently. Typically, estimation
of both the Euclidean and the Banach parameter is necessary in order to
describe the random phenomenon under study to a sufficient extent. Since
efficient estimators are asymptotically linear, the above substitution method
is a particular case of substituting asymptotically linear estimators of a
Euclidean parameter into estimators that are asymptotically linear themselves
and that depend on this Euclidean parameter. This more general substitution
case is studied for its own sake as well, and a hereditary property for
asymptotic linearity is proved.Comment: Published at http://dx.doi.org/10.1214/009053604000000913 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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