5,264 research outputs found
Nearly Optimal Private Convolution
We study computing the convolution of a private input with a public input
, while satisfying the guarantees of -differential
privacy. Convolution is a fundamental operation, intimately related to Fourier
Transforms. In our setting, the private input may represent a time series of
sensitive events or a histogram of a database of confidential personal
information. Convolution then captures important primitives including linear
filtering, which is an essential tool in time series analysis, and aggregation
queries on projections of the data.
We give a nearly optimal algorithm for computing convolutions while
satisfying -differential privacy. Surprisingly, we follow
the simple strategy of adding independent Laplacian noise to each Fourier
coefficient and bounding the privacy loss using the composition theorem of
Dwork, Rothblum, and Vadhan. We derive a closed form expression for the optimal
noise to add to each Fourier coefficient using convex programming duality. Our
algorithm is very efficient -- it is essentially no more computationally
expensive than a Fast Fourier Transform.
To prove near optimality, we use the recent discrepancy lowerbounds of
Muthukrishnan and Nikolov and derive a spectral lower bound using a
characterization of discrepancy in terms of determinants
Faster truncated integer multiplication
We present new algorithms for computing the low n bits or the high n bits of
the product of two n-bit integers. We show that these problems may be solved in
asymptotically 75% of the time required to compute the full 2n-bit product,
assuming that the underlying integer multiplication algorithm relies on
computing cyclic convolutions of real sequences.Comment: 28 page
Improvements on "Fast space-variant elliptical filtering using box splines"
It is well-known that box filters can be efficiently computed using
pre-integrations and local finite-differences
[Crow1984,Heckbert1986,Viola2001]. By generalizing this idea and by combining
it with a non-standard variant of the Central Limit Theorem, a constant-time or
O(1) algorithm was proposed in [Chaudhury2010] that allowed one to perform
space-variant filtering using Gaussian-like kernels. The algorithm was based on
the observation that both isotropic and anisotropic Gaussians could be
approximated using certain bivariate splines called box splines. The attractive
feature of the algorithm was that it allowed one to continuously control the
shape and size (covariance) of the filter, and that it had a fixed
computational cost per pixel, irrespective of the size of the filter. The
algorithm, however, offered a limited control on the covariance and accuracy of
the Gaussian approximation. In this work, we propose some improvements by
appropriately modifying the algorithm in [Chaudhury2010].Comment: 7 figure
Network Sketching: Exploiting Binary Structure in Deep CNNs
Convolutional neural networks (CNNs) with deep architectures have
substantially advanced the state-of-the-art in computer vision tasks. However,
deep networks are typically resource-intensive and thus difficult to be
deployed on mobile devices. Recently, CNNs with binary weights have shown
compelling efficiency to the community, whereas the accuracy of such models is
usually unsatisfactory in practice. In this paper, we introduce network
sketching as a novel technique of pursuing binary-weight CNNs, targeting at
more faithful inference and better trade-off for practical applications. Our
basic idea is to exploit binary structure directly in pre-trained filter banks
and produce binary-weight models via tensor expansion. The whole process can be
treated as a coarse-to-fine model approximation, akin to the pencil drawing
steps of outlining and shading. To further speedup the generated models, namely
the sketches, we also propose an associative implementation of binary tensor
convolutions. Experimental results demonstrate that a proper sketch of AlexNet
(or ResNet) outperforms the existing binary-weight models by large margins on
the ImageNet large scale classification task, while the committed memory for
network parameters only exceeds a little.Comment: To appear in CVPR201
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