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72
Learning Nondeterministic Mealy Machines
"... In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy mac ..."
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Cited by 1 (1 self)
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In applications where abstract models of reactive systems are to be inferred, one important challenge is that the behavior of such systems can be inherently nondeterministic. To cope with this challenge, we developed an algorithm to infer nondeterministic computation models in the form of Mealy
NonDeterministic Kleene Coalgebras
"... In this paper, we present a systematic way of deriving (1) languages of (generalised) regular expressions, and (2) sound and complete axiomatizations thereof, for a wide variety of systems. This generalizes both the results of Kleene (on regular languages and deterministic finite automata) and Miln ..."
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Cited by 25 (9 self)
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) and Milner (on regular behaviours and finite labelled transition systems), and includes many other systems such as Mealy and Moore machines.
Beyond Mealy machines: Learning translators with recurrent neural networks
 In Proceedings of the World Conference on Neural Networks '96
, 1996
"... Recent work has shown that recurrent neural networks can be trained to behave as finitestate automata from samples of input strings and their corresponding outputs. However, most of the work has focused on training simple networks to behave as the simplest class of deterministic machines, Mealy ..."
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Recent work has shown that recurrent neural networks can be trained to behave as finitestate automata from samples of input strings and their corresponding outputs. However, most of the work has focused on training simple networks to behave as the simplest class of deterministic machines, Mealy
Learning Machines Implemented on NonDeterministic Hardware
"... This paper highlights new opportunities for designing largescale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and applicationlevel practitioners to stay – for the most part – oblivious to the details of the underlying hardware ..."
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This paper highlights new opportunities for designing largescale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and applicationlevel practitioners to stay – for the most part – oblivious to the details of the underlying hardware
Improving Active Mealy Machine Learning for Protocol Conformance Testing
 MACHINE LEARNING
"... Using a wellknown industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model MR ..."
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Cited by 1 (0 self)
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Using a wellknown industrial case study from the verification literature, the bounded retransmission protocol, we show how active learning can be used to establish the correctness of protocol implementation I relative to a given reference implementation R. Using active learning, we learn a model
Learning and Extracting Initial Mealy Automata With a Modular Neural Network Model
 NEURAL COMPUTATION
, 1995
"... A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive ..."
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Cited by 16 (0 self)
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A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive
Automatic Equivalence Proofs for Nondeterministic
, 1303
"... A notion of generalized regular expressions for a large class of systems modeled as coalgebras, and an analogue of Kleene’s theorem and Kleene algebra, were recently proposed by a subset of the authors of this paper. Examples of the systems covered include infinite streams, deterministic automata, M ..."
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, Mealy machines and labelled transition systems. In this paper, we present a novel algorithm to decide whether two expressions are bisimilar or not. The procedure is implemented in the automatic theorem prover CIRC, by reducing coinduction to an entailment relation between an algebraic specification
Learning Nondeterministic Classifiers Juan José
"... Nondeterministic classifiers are defined as those allowed to predict more than one class for some entries from an input space. Given that the true class should be included in predictions and the number of classes predicted should be as small as possible, these kind of classifiers can be considered a ..."
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as Information Retrieval (IR) procedures. In this paper, we propose a family of IR loss functions to measure the performance of nondeterministic learners. After discussing such measures, we derive an algorithm for learning optimal nondeterministic hypotheses. Given an entry from the input space, the algorithm
Factorizing YAGO: scalable machine learning for linked data
 In WWW
, 2012
"... Vast amounts of structured information have been published in the Semantic Web’s Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD’s size, partial data inconsistencies and inherent noisi ..."
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Cited by 49 (15 self)
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noisiness. Machine Learning offers an alternative approach to exploiting LOD’s data with the advantages that Machine Learning algorithms are typically robust to both noise and data inconsistencies and are able to efficiently utilize nondeterministic dependencies in the data. From a Machine Learning point
Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks
"... Abstract. The paper investigates modification of backpropagation algorithm, consisting of discretization of neural network weights after each training cycle. This modification, aimed at overfitting reduction, restricts the set of possible values of weights to a discrete subset of real numbers, leadi ..."
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to original value. In this way, global behavior of original algorithm is preserved. The presented method of discretization is general and may be applied to other machinelearning algorithms. It is also an example of how an algorithm for continuous optimization can be successfully applied to optimization over
Results 1  10
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