3,091 research outputs found
On the Inductive Bias of Neural Tangent Kernels
State-of-the-art neural networks are heavily over-parameterized, making the
optimization algorithm a crucial ingredient for learning predictive models with
good generalization properties. A recent line of work has shown that in a
certain over-parameterized regime, the learning dynamics of gradient descent
are governed by a certain kernel obtained at initialization, called the neural
tangent kernel. We study the inductive bias of learning in such a regime by
analyzing this kernel and the corresponding function space (RKHS). In
particular, we study smoothness, approximation, and stability properties of
functions with finite norm, including stability to image deformations in the
case of convolutional networks, and compare to other known kernels for similar
architectures.Comment: NeurIPS 201
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
On the convergence of mirror descent beyond stochastic convex programming
In this paper, we examine the convergence of mirror descent in a class of
stochastic optimization problems that are not necessarily convex (or even
quasi-convex), and which we call variationally coherent. Since the standard
technique of "ergodic averaging" offers no tangible benefits beyond convex
programming, we focus directly on the algorithm's last generated sample (its
"last iterate"), and we show that it converges with probabiility if the
underlying problem is coherent. We further consider a localized version of
variational coherence which ensures local convergence of stochastic mirror
descent (SMD) with high probability. These results contribute to the landscape
of non-convex stochastic optimization by showing that (quasi-)convexity is not
essential for convergence to a global minimum: rather, variational coherence, a
much weaker requirement, suffices. Finally, building on the above, we reveal an
interesting insight regarding the convergence speed of SMD: in problems with
sharp minima (such as generic linear programs or concave minimization
problems), SMD reaches a minimum point in a finite number of steps (a.s.), even
in the presence of persistent gradient noise. This result is to be contrasted
with existing black-box convergence rate estimates that are only asymptotic.Comment: 30 pages, 5 figure
MV3: A new word based stream cipher using rapid mixing and revolving buffers
MV3 is a new word based stream cipher for encrypting long streams of data. A
direct adaptation of a byte based cipher such as RC4 into a 32- or 64-bit word
version will obviously need vast amounts of memory. This scaling issue
necessitates a look for new components and principles, as well as mathematical
analysis to justify their use. Our approach, like RC4's, is based on rapidly
mixing random walks on directed graphs (that is, walks which reach a random
state quickly, from any starting point). We begin with some well understood
walks, and then introduce nonlinearity in their steps in order to improve
security and show long term statistical correlations are negligible. To
minimize the short term correlations, as well as to deter attacks using
equations involving successive outputs, we provide a method for sequencing the
outputs derived from the walk using three revolving buffers. The cipher is fast
-- it runs at a speed of less than 5 cycles per byte on a Pentium IV processor.
A word based cipher needs to output more bits per step, which exposes more
correlations for attacks. Moreover we seek simplicity of construction and
transparent analysis. To meet these requirements, we use a larger state and
claim security corresponding to only a fraction of it. Our design is for an
adequately secure word-based cipher; our very preliminary estimate puts the
security close to exhaustive search for keys of size < 256 bits.Comment: 27 pages, shortened version will appear in "Topics in Cryptology -
CT-RSA 2007
Model Checker Execution Reports
Software model checking constitutes an undecidable problem and, as such, even
an ideal tool will in some cases fail to give a conclusive answer. In practice,
software model checkers fail often and usually do not provide any information
on what was effectively checked. The purpose of this work is to provide a
conceptual framing to extend software model checkers in a way that allows users
to access information about incomplete checks. We characterize the information
that model checkers themselves can provide, in terms of analyzed traces, i.e.
sequences of statements, and safe cones, and present the notion of execution
reports, which we also formalize. We instantiate these concepts for a family of
techniques based on Abstract Reachability Trees and implement the approach
using the software model checker CPAchecker. We evaluate our approach
empirically and provide examples to illustrate the execution reports produced
and the information that can be extracted
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