31,648 research outputs found
Stochastic Dynamic Cache Partitioning for Encrypted Content Delivery
In-network caching is an appealing solution to cope with the increasing
bandwidth demand of video, audio and data transfer over the Internet.
Nonetheless, an increasing share of content delivery services adopt encryption
through HTTPS, which is not compatible with traditional ISP-managed approaches
like transparent and proxy caching. This raises the need for solutions
involving both Internet Service Providers (ISP) and Content Providers (CP): by
design, the solution should preserve business-critical CP information (e.g.,
content popularity, user preferences) on the one hand, while allowing for a
deeper integration of caches in the ISP architecture (e.g., in 5G femto-cells)
on the other hand.
In this paper we address this issue by considering a content-oblivious
ISP-operated cache. The ISP allocates the cache storage to various content
providers so as to maximize the bandwidth savings provided by the cache: the
main novelty lies in the fact that, to protect business-critical information,
ISPs only need to measure the aggregated miss rates of the individual CPs and
do not need to be aware of the objects that are requested, as in classic
caching. We propose a cache allocation algorithm based on a perturbed
stochastic subgradient method, and prove that the algorithm converges close to
the allocation that maximizes the overall cache hit rate. We use extensive
simulations to validate the algorithm and to assess its convergence rate under
stationary and non-stationary content popularity. Our results (i) testify the
feasibility of content-oblivious caches and (ii) show that the proposed
algorithm can achieve within 10\% from the global optimum in our evaluation
A General Optimization Technique for High Quality Community Detection in Complex Networks
Recent years have witnessed the development of a large body of algorithms for
community detection in complex networks. Most of them are based upon the
optimization of objective functions, among which modularity is the most common,
though a number of alternatives have been suggested in the scientific
literature. We present here an effective general search strategy for the
optimization of various objective functions for community detection purposes.
When applied to modularity, on both real-world and synthetic networks, our
search strategy substantially outperforms the best existing algorithms in terms
of final scores of the objective function; for description length, its
performance is on par with the original Infomap algorithm. The execution time
of our algorithm is on par with non-greedy alternatives present in literature,
and networks of up to 10,000 nodes can be analyzed in time spans ranging from
minutes to a few hours on average workstations, making our approach readily
applicable to tasks which require the quality of partitioning to be as high as
possible, and are not limited by strict time constraints. Finally, based on the
most effective of the available optimization techniques, we compare the
performance of modularity and code length as objective functions, in terms of
the quality of the partitions one can achieve by optimizing them. To this end,
we evaluated the ability of each objective function to reconstruct the
underlying structure of a large set of synthetic and real-world networks.Comment: MAIN text: 14 pages, 4 figures, 1 table Supplementary information: 19
pages, 8 figures, 5 table
Jump-Diffusion Approximation of Stochastic Reaction Dynamics: Error bounds and Algorithms
Biochemical reactions can happen on different time scales and also the
abundance of species in these reactions can be very different from each other.
Classical approaches, such as deterministic or stochastic approach, fail to
account for or to exploit this multi-scale nature, respectively. In this paper,
we propose a jump-diffusion approximation for multi-scale Markov jump processes
that couples the two modeling approaches. An error bound of the proposed
approximation is derived and used to partition the reactions into fast and slow
sets, where the fast set is simulated by a stochastic differential equation and
the slow set is modeled by a discrete chain. The error bound leads to a very
efficient dynamic partitioning algorithm which has been implemented for several
multi-scale reaction systems. The gain in computational efficiency is
illustrated by a realistically sized model of a signal transduction cascade
coupled to a gene expression dynamics.Comment: 32 pages, 7 figure
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
Asynchronous distributed algorithms are a popular way to reduce
synchronization costs in large-scale optimization, and in particular for neural
network training. However, for nonsmooth and nonconvex objectives, few
convergence guarantees exist beyond cases where closed-form proximal operator
solutions are available. As most popular contemporary deep neural networks lead
to nonsmooth and nonconvex objectives, there is now a pressing need for such
convergence guarantees. In this paper, we analyze for the first time the
convergence of stochastic asynchronous optimization for this general class of
objectives. In particular, we focus on stochastic subgradient methods allowing
for block variable partitioning, where the shared-memory-based model is
asynchronously updated by concurrent processes. To this end, we first introduce
a probabilistic model which captures key features of real asynchronous
scheduling between concurrent processes; under this model, we establish
convergence with probability one to an invariant set for stochastic subgradient
methods with momentum.
From the practical perspective, one issue with the family of methods we
consider is that it is not efficiently supported by machine learning
frameworks, as they mostly focus on distributed data-parallel strategies. To
address this, we propose a new implementation strategy for shared-memory based
training of deep neural networks, whereby concurrent parameter servers are
utilized to train a partitioned but shared model in single- and multi-GPU
settings. Based on this implementation, we achieve on average 1.2x speed-up in
comparison to state-of-the-art training methods for popular image
classification tasks without compromising accuracy
Stochastic Training of Neural Networks via Successive Convex Approximations
This paper proposes a new family of algorithms for training neural networks
(NNs). These are based on recent developments in the field of non-convex
optimization, going under the general name of successive convex approximation
(SCA) techniques. The basic idea is to iteratively replace the original
(non-convex, highly dimensional) learning problem with a sequence of (strongly
convex) approximations, which are both accurate and simple to optimize.
Differently from similar ideas (e.g., quasi-Newton algorithms), the
approximations can be constructed using only first-order information of the
neural network function, in a stochastic fashion, while exploiting the overall
structure of the learning problem for a faster convergence. We discuss several
use cases, based on different choices for the loss function (e.g., squared loss
and cross-entropy loss), and for the regularization of the NN's weights. We
experiment on several medium-sized benchmark problems, and on a large-scale
dataset involving simulated physical data. The results show how the algorithm
outperforms state-of-the-art techniques, providing faster convergence to a
better minimum. Additionally, we show how the algorithm can be easily
parallelized over multiple computational units without hindering its
performance. In particular, each computational unit can optimize a tailored
surrogate function defined on a randomly assigned subset of the input
variables, whose dimension can be selected depending entirely on the available
computational power.Comment: Preprint submitted to IEEE Transactions on Neural Networks and
Learning System
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