805 research outputs found
MAT learners for recognizable tree languages and tree series
We review a family of closely related query learning algorithms for unweighted and weighted tree automata, all of which are based on adaptations of the minimal adequate teacher (MAT) model by Angluin. Rather than presenting
new results, the goal is to discuss these algorithms in sufficient detail to make their similarities and differences transparent to the reader interested in grammatical inference of tree automata
Series, Weighted Automata, Probabilistic Automata and Probability Distributions for Unranked Trees.
We study tree series and weighted tree automata over unranked trees. The message is that recognizable tree series for unranked trees can be defined and studied from recognizable tree series for binary representations of unranked trees. For this we prove results of Denis et al (2007) as follows. We extend hedge automata -- a class of tree automata for unranked trees -- to weighted hedge automata. We define weighted stepwise automata as weighted tree automata for binary representations of unranked trees. We show that recognizable tree series can be equivalently defined by weighted hedge automata or weighted stepwise automata. Then we consider real-valued tree series and weighted tree automata over the field of real numbers. We show that the result also holds for probabilistic automata -- weighted automata with normalisation conditions for rules. We also define convergent tree series and show that convergence properties for recognizable tree series are preserved via binary encoding. From Etessami and Yannakakis (2009), we present decidability results on probabilistic tree automata and algorithms for computing sums of convergent series. Last we show that streaming algorithms for unranked trees can be seen as slight transformations of algorithms on the binary representations
Visualizing and Understanding Sum-Product Networks
Sum-Product Networks (SPNs) are recently introduced deep tractable
probabilistic models by which several kinds of inference queries can be
answered exactly and in a tractable time. Up to now, they have been largely
used as black box density estimators, assessed only by comparing their
likelihood scores only. In this paper we explore and exploit the inner
representations learned by SPNs. We do this with a threefold aim: first we want
to get a better understanding of the inner workings of SPNs; secondly, we seek
additional ways to evaluate one SPN model and compare it against other
probabilistic models, providing diagnostic tools to practitioners; lastly, we
want to empirically evaluate how good and meaningful the extracted
representations are, as in a classic Representation Learning framework. In
order to do so we revise their interpretation as deep neural networks and we
propose to exploit several visualization techniques on their node activations
and network outputs under different types of inference queries. To investigate
these models as feature extractors, we plug some SPNs, learned in a greedy
unsupervised fashion on image datasets, in supervised classification learning
tasks. We extract several embedding types from node activations by filtering
nodes by their type, by their associated feature abstraction level and by their
scope. In a thorough empirical comparison we prove them to be competitive
against those generated from popular feature extractors as Restricted Boltzmann
Machines. Finally, we investigate embeddings generated from random
probabilistic marginal queries as means to compare other tractable
probabilistic models on a common ground, extending our experiments to Mixtures
of Trees.Comment: Machine Learning Journal paper (First Online), 24 page
Weighted Tree Automata -- May it be a little more?
This is a book on weighted tree automata. We present the basic definitions
and some of the important results in a coherent form with full proofs. The
concept of weighted tree automata is part of Automata Theory and it touches the
area of Universal Algebra. It originated from two sources: weighted string
automata and finite-state tree automata
Tree Transducers and Formal Methods (Dagstuhl Seminar 13192)
The aim of this Dagstuhl Seminar was to bring together researchers from various research areas related to the theory and application of tree transducers. Recently, interest in tree transducers has been revived due to surprising new applications in areas such as XML databases, security verification, programming language theory, and linguistics. This seminar therefore aimed to inspire the exchange of theoretical results and information regarding the practical requirements related to tree transducers
Constructs and evaluation strategies for intelligent speculative parallelism - armageddon revisited
This report addresses speculative parallelism (the assignment of spare processing resources to tasks which are not known to be strictly required for the successful completion of a computation) at the user and application level. At this level, the execution of a program is seen
as a (dynamic) tree —a graph, in general. A solution for a problem is a traversal of this graph from the initial state to a node known to be the answer. Speculative parallelism then represents the assignment of resources to múltiple branches of this graph even if they are not positively known to be on the path to a solution. In highly non-deterministic programs the branching factor can be very high and a naive assignment will very soon use up all the
resources. This report presents work assignment strategies other than the usual depth-first and breadth-first. Instead, best-first strategies are used. Since their definition is application-dependent, the application language contains primitives that allow the user (or application programmer) to a) indÃcate when intelligent OR-parallelism should be used; b) provide the functions that define "best," and c) indÃcate when to use them.
An abstract architecture enables those primitives to perform the search in a "speculative" way, using several processors, synchronizing them, killing the siblings of the path leading to the answer, etc. The user is freed from worrying about these interactions. Several search strategies are proposed and their implementation issues are addressed. "Armageddon," a global pruning method, is introduced, together with both a software and a hardware implementation for it. The concepts exposed are applicable to áreas of Artificial Intelligence such as extensive expert systems, planning, game playing, and in general to large search problems. The proposed strategies, although
showing promise, have not been evaluated by simulation or experimentation
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