146,739 research outputs found
Complexity-class-encoding sets
Properties of sets which are complex because they encode complexity classes areexplored. It is shown that not all sets with inherent complexity are of this type, although this is the only type of set for which well-developed techniques exist for proving inherent complexity.Possibilities for the complexity of encoding sets are discussed, first with referenceto an “almost everywhere” vs. “infinitely many arguments” classification, and later with reference to the density of the set of arguments on which the problem is complex.It is shown that relative complexity relationships among sets of this type are highlystructured, in contrast to the wide variation possible among arbitrary recursive sets
Signal Set Design for Full-Diversity Low-Decoding-Complexity Differential Scaled-Unitary STBCs
The problem of designing high rate, full diversity noncoherent space-time
block codes (STBCs) with low encoding and decoding complexity is addressed.
First, the notion of -group encodable and -group decodable linear STBCs
is introduced. Then for a known class of rate-1 linear designs, an explicit
construction of fully-diverse signal sets that lead to four-group encodable and
four-group decodable differential scaled unitary STBCs for any power of two
number of antennas is provided. Previous works on differential STBCs either
sacrifice decoding complexity for higher rate or sacrifice rate for lower
decoding complexity.Comment: 5 pages, 2 figures. To appear in Proceedings of IEEE ISIT 2007, Nice,
Franc
Memoization for Unary Logic Programming: Characterizing PTIME
We give a characterization of deterministic polynomial time computation based
on an algebraic structure called the resolution semiring, whose elements can be
understood as logic programs or sets of rewriting rules over first-order terms.
More precisely, we study the restriction of this framework to terms (and logic
programs, rewriting rules) using only unary symbols. We prove it is complete
for polynomial time computation, using an encoding of pushdown automata. We
then introduce an algebraic counterpart of the memoization technique in order
to show its PTIME soundness. We finally relate our approach and complexity
results to complexity of logic programming. As an application of our
techniques, we show a PTIME-completeness result for a class of logic
programming queries which use only unary function symbols.Comment: Soumis {\`a} LICS 201
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
The complexity of a learning task is increased by transformations in the input space that preserve class identity. Visual object recognition for example is affected by changes in viewpoint, scale, illumination or planar transformations. While drastically altering the visual appearance, these changes are orthogonal to recognition and should not be reflected in the representation or feature encoding used for learning. We introduce a framework for weakly supervised learning of image embeddings that are robust to transformations and selective to the class distribution, using sets of transforming examples (orbit sets), deep parametrizations and a novel orbit-based loss. The proposed loss combines a discriminative, contrastive part for orbits with a reconstruction error that learns to rectify orbit transformations. The learned embeddings are evaluated in distance metric-based tasks, such as one-shot classification under geometric transformations, as well as face verification and retrieval under more realistic visual variability. Our results suggest that orbit sets, suitably computed or observed, can be used for efficient, weakly-supervised learning of semantically relevant image embeddings.This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216
On the Parameterized Intractability of Monadic Second-Order Logic
One of Courcelle's celebrated results states that if C is a class of graphs
of bounded tree-width, then model-checking for monadic second order logic
(MSO_2) is fixed-parameter tractable (fpt) on C by linear time parameterized
algorithms, where the parameter is the tree-width plus the size of the formula.
An immediate question is whether this is best possible or whether the result
can be extended to classes of unbounded tree-width. In this paper we show that
in terms of tree-width, the theorem cannot be extended much further. More
specifically, we show that if C is a class of graphs which is closed under
colourings and satisfies certain constructibility conditions and is such that
the tree-width of C is not bounded by \log^{84} n then MSO_2-model checking is
not fpt unless SAT can be solved in sub-exponential time. If the tree-width of
C is not poly-logarithmically bounded, then MSO_2-model checking is not fpt
unless all problems in the polynomial-time hierarchy can be solved in
sub-exponential time
Interpretable multiclass classification by MDL-based rule lists
Interpretable classifiers have recently witnessed an increase in attention
from the data mining community because they are inherently easier to understand
and explain than their more complex counterparts. Examples of interpretable
classification models include decision trees, rule sets, and rule lists.
Learning such models often involves optimizing hyperparameters, which typically
requires substantial amounts of data and may result in relatively large models.
In this paper, we consider the problem of learning compact yet accurate
probabilistic rule lists for multiclass classification. Specifically, we
propose a novel formalization based on probabilistic rule lists and the minimum
description length (MDL) principle. This results in virtually parameter-free
model selection that naturally allows to trade-off model complexity with
goodness of fit, by which overfitting and the need for hyperparameter tuning
are effectively avoided. Finally, we introduce the Classy algorithm, which
greedily finds rule lists according to the proposed criterion. We empirically
demonstrate that Classy selects small probabilistic rule lists that outperform
state-of-the-art classifiers when it comes to the combination of predictive
performance and interpretability. We show that Classy is insensitive to its
only parameter, i.e., the candidate set, and that compression on the training
set correlates with classification performance, validating our MDL-based
selection criterion
A new Lenstra-type Algorithm for Quasiconvex Polynomial Integer Minimization with Complexity 2^O(n log n)
We study the integer minimization of a quasiconvex polynomial with
quasiconvex polynomial constraints. We propose a new algorithm that is an
improvement upon the best known algorithm due to Heinz (Journal of Complexity,
2005). This improvement is achieved by applying a new modern Lenstra-type
algorithm, finding optimal ellipsoid roundings, and considering sparse
encodings of polynomials. For the bounded case, our algorithm attains a
time-complexity of s (r l M d)^{O(1)} 2^{2n log_2(n) + O(n)} when M is a bound
on the number of monomials in each polynomial and r is the binary encoding
length of a bound on the feasible region. In the general case, s l^{O(1)}
d^{O(n)} 2^{2n log_2(n) +O(n)}. In each we assume d>= 2 is a bound on the total
degree of the polynomials and l bounds the maximum binary encoding size of the
input.Comment: 28 pages, 10 figure
The DLV System for Knowledge Representation and Reasoning
This paper presents the DLV system, which is widely considered the
state-of-the-art implementation of disjunctive logic programming, and addresses
several aspects. As for problem solving, we provide a formal definition of its
kernel language, function-free disjunctive logic programs (also known as
disjunctive datalog), extended by weak constraints, which are a powerful tool
to express optimization problems. We then illustrate the usage of DLV as a tool
for knowledge representation and reasoning, describing a new declarative
programming methodology which allows one to encode complex problems (up to
-complete problems) in a declarative fashion. On the foundational
side, we provide a detailed analysis of the computational complexity of the
language of DLV, and by deriving new complexity results we chart a complete
picture of the complexity of this language and important fragments thereof.
Furthermore, we illustrate the general architecture of the DLV system which
has been influenced by these results. As for applications, we overview
application front-ends which have been developed on top of DLV to solve
specific knowledge representation tasks, and we briefly describe the main
international projects investigating the potential of the system for industrial
exploitation. Finally, we report about thorough experimentation and
benchmarking, which has been carried out to assess the efficiency of the
system. The experimental results confirm the solidity of DLV and highlight its
potential for emerging application areas like knowledge management and
information integration.Comment: 56 pages, 9 figures, 6 table
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