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Quantitative analysis of probabilistic pushdown automata: . . .
, 2005
"... Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with rcursive procedure calls. Previous works considered the decidability and complexity of the modelchecking problem for pPDA and various probabilistic temporal logics. In this paper we concen ..."
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Cited by 41 (14 self)
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concentrate on computing the expected values and variances of various random variables defined over runs of a given probabilistic pushdown automaton. In particular, we show how to compute the expected accumulated reward and the expected gain for certain classes of reward functions. Using these results, we
The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations
, 1993
"... In order for neural networks to learn complex languages or grammars, they must have sufficient computational power or resources to recognize or generate such languages. Though many approaches to effectively utilizing the computational power of neural networks have been discussed, an obvious one is t ..."
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Cited by 18 (2 self)
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is the probabilistic storage of and action on data. After training on sample strings of an unknown source grammar, a quantization procedure extracts from the analog stack and neural network a discrete pushdown automata (PDA). Simulations show that in learning deterministic contextfree grammars the balanced
Quantitative Analysis of Probabilistic Pushdown Automata:Expectations and Variances
"... Abstract Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with recursive procedure calls. Previous works considered the decidability and complexity of the modelchecking problem for pPDA and various probabilistic temporal logics. In this pa ..."
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per we concentrate on computing the expected values and variances of various random variables defined over runsof a given probabilistic pushdown automaton. In particular, we show how to compute the expected accumulated reward and the expected gain for certain classes of reward functions. Using
Relating Probabilistic Grammars and Automata
 In Proceedings of ACP’99
, 1999
"... Both probabilistic contextfree grammars (PCFGs) and shiftreduce probabilistic pushdown automata (PPDAs) have been used for language modeling and maximum likelihood parsing. We investigate the precise relationship between these two formalisms, showing that, while they define the same classes of pr ..."
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Cited by 43 (0 self)
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Both probabilistic contextfree grammars (PCFGs) and shiftreduce probabilistic pushdown automata (PPDAs) have been used for language modeling and maximum likelihood parsing. We investigate the precise relationship between these two formalisms, showing that, while they define the same classes
SingleLetter Languages Accepted By Alternating and Probabilistic Pushdown Automata
"... We consider 1way ktape alternating pushdown automata (kapa) and 1way ktape alternating finite automata (kafa). We say that an alternating automaton accepts a language L with f(n)bounded maximal (respectively, minimal) leafsize if arbitrary (respectively, at least one) accepting tree for any ..."
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We consider 1way ktape alternating pushdown automata (kapa) and 1way ktape alternating finite automata (kafa). We say that an alternating automaton accepts a language L with f(n)bounded maximal (respectively, minimal) leafsize if arbitrary (respectively, at least one) accepting tree
SPEDE: Probabilistic edit distance . . .
"... This paper describes Stanford University’s submission to the Shared Evaluation Task of WMT... probabilistic edit distance as predictions of translation quality. We learn weighted edit distance in a probabilistic finite state machine (pFSM) model, where state transitions correspond to edit operation ..."
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operations. While standard edit distance models cannot capture longdistance word swapping or cross alignments, we rectify these shortcomings using a novel pushdown automaton extension of the pFSM model. Our models are trained in a regression framework, and can easily incorporate a rich set of linguistic
PAPER Expressive Power of Quantum Pushdown Automata with Classical Stack Operations under the PerfectSoundness Condition
"... SUMMARY One important question for quantum computing is whether a computational gap exists between models that are allowed to use quantum effects and models that are not. Several types of quantum computation models have been proposed, including quantum finite automata and quantum pushdown automata ( ..."
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Cited by 2 (0 self)
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of quantum pushdown automata whose stacks are assumed to be implemented as classical devices, and show that they are strictly more powerful than their classical counterparts under the perfectsoundness condition, where perfectsoundness means that an automaton never accepts a word that is not in the language
SPEDE: Probabilistic edit distance metrics for . . .
, 2012
"... This paper describes Stanford University’s submission ... probabilistic edit distance as predictions of translation quality. We learn weighted edit distance in a probabilistic finite state machine (pFSM) model, where state transitions correspond to edit operations. While standard edit distance model ..."
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Cited by 4 (1 self)
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models cannot capture longdistance word swapping or cross alignments, we rectify these shortcomings using a novel pushdown automaton extension of the pFSM model. Our models are trained in a regression framework, and can easily incorporate a rich set of linguistic features. Evaluated on two different
Model Checking of Recursive Probabilistic Systems
"... Recursive Markov Chains (RMCs) are a natural abstract model of procedural probabilistic programs and related systems involving recursion and probability. They succinctly define a class of denumerable Markov chains that generalize several other stochastic models, and they are equivalent in a precise ..."
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Cited by 4 (0 self)
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sense to probabilistic Pushdown Systems. In this paper, we study the problem of model checking an RMC against an ωregular specification, given in terms of a Büchi automaton or a Linear Temporal Logic (LTL) formula. Namely, given an RMC A and a property we wish to know the probability that an execution
Probabilistic Finite State Machines for Regressionbased MT Evaluation
"... Accurate and robust metrics for automatic evaluation are key to the development of statistical machine translation (MT) systems. We first introduce a new regression model that uses a probabilistic finite state machine (pFSM) to compute weighted edit distance as predictions of translation quality. We ..."
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. We also propose a novel pushdown automaton extension of the pFSM model for modeling word swapping and cross alignments that cannot be captured by standard edit distance models. Our models can easily incorporate a rich set of linguistic features, and automatically learn their weights, eliminating
Results 1  10
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