60,244 research outputs found
Reconfigurable interconnects in DSM systems: a focus on context switch behavior
Recent advances in the development of reconfigurable optical interconnect technologies allow for the fabrication of low cost and run-time adaptable interconnects in large distributed shared-memory (DSM) multiprocessor machines. This can allow the use of adaptable interconnection networks that alleviate the huge bottleneck present due to the gap between the processing speed and the memory access time over the network. In this paper we have studied the scheduling of tasks by the kernel of the operating system (OS) and its influence on communication between the processing nodes of the system, focusing on the traffic generated just after a context switch. We aim to use these results as a basis to propose a potential reconfiguration of the network that could provide a significant speedup
Hedging predictions in machine learning
Recent advances in machine learning make it possible to design efficient
prediction algorithms for data sets with huge numbers of parameters. This paper
describes a new technique for "hedging" the predictions output by many such
algorithms, including support vector machines, kernel ridge regression, kernel
nearest neighbours, and by many other state-of-the-art methods. The hedged
predictions for the labels of new objects include quantitative measures of
their own accuracy and reliability. These measures are provably valid under the
assumption of randomness, traditional in machine learning: the objects and
their labels are assumed to be generated independently from the same
probability distribution. In particular, it becomes possible to control (up to
statistical fluctuations) the number of erroneous predictions by selecting a
suitable confidence level. Validity being achieved automatically, the remaining
goal of hedged prediction is efficiency: taking full account of the new
objects' features and other available information to produce as accurate
predictions as possible. This can be done successfully using the powerful
machinery of modern machine learning.Comment: 24 pages; 9 figures; 2 tables; a version of this paper (with
discussion and rejoinder) is to appear in "The Computer Journal
HyBIS: Windows Guest Protection through Advanced Memory Introspection
Effectively protecting the Windows OS is a challenging task, since most
implementation details are not publicly known. Windows has always been the main
target of malwares that have exploited numerous bugs and vulnerabilities.
Recent trusted boot and additional integrity checks have rendered the Windows
OS less vulnerable to kernel-level rootkits. Nevertheless, guest Windows
Virtual Machines are becoming an increasingly interesting attack target. In
this work we introduce and analyze a novel Hypervisor-Based Introspection
System (HyBIS) we developed for protecting Windows OSes from malware and
rootkits. The HyBIS architecture is motivated and detailed, while targeted
experimental results show its effectiveness. Comparison with related work
highlights main HyBIS advantages such as: effective semantic introspection,
support for 64-bit architectures and for latest Windows (8.x and 10), advanced
malware disabling capabilities. We believe the research effort reported here
will pave the way to further advances in the security of Windows OSes
Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterization
of several deep learning models. Given only a few weight values for each
feature it is possible to accurately predict the remaining values. Moreover, we
show that not only can the parameter values be predicted, but many of them need
not be learned at all. We train several different architectures by learning
only a small number of weights and predicting the rest. In the best case we are
able to predict more than 95% of the weights of a network without any drop in
accuracy
Evolino for recurrent support vector machines
Traditional Support Vector Machines (SVMs) need pre-wired finite time windows
to predict and classify time series. They do not have an internal state
necessary to deal with sequences involving arbitrary long-term dependencies.
Here we introduce a new class of recurrent, truly sequential SVM-like devices
with internal adaptive states, trained by a novel method called EVOlution of
systems with KErnel-based outputs (Evoke), an instance of the recent Evolino
class of methods. Evoke evolves recurrent neural networks to detect and
represent temporal dependencies while using quadratic programming/support
vector regression to produce precise outputs. Evoke is the first SVM-based
mechanism learning to classify a context-sensitive language. It also
outperforms recent state-of-the-art gradient-based recurrent neural networks
(RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure
Positive Definite Kernels in Machine Learning
This survey is an introduction to positive definite kernels and the set of
methods they have inspired in the machine learning literature, namely kernel
methods. We first discuss some properties of positive definite kernels as well
as reproducing kernel Hibert spaces, the natural extension of the set of
functions associated with a kernel defined
on a space . We discuss at length the construction of kernel
functions that take advantage of well-known statistical models. We provide an
overview of numerous data-analysis methods which take advantage of reproducing
kernel Hilbert spaces and discuss the idea of combining several kernels to
improve the performance on certain tasks. We also provide a short cookbook of
different kernels which are particularly useful for certain data-types such as
images, graphs or speech segments.Comment: draft. corrected a typo in figure
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427
- …