1,937 research outputs found
Sampling from Stochastic Finite Automata with Applications to CTC Decoding
Stochastic finite automata arise naturally in many language and speech
processing tasks. They include stochastic acceptors, which represent certain
probability distributions over random strings. We consider the problem of
efficient sampling: drawing random string variates from the probability
distribution represented by stochastic automata and transformations of those.
We show that path-sampling is effective and can be efficient if the
epsilon-graph of a finite automaton is acyclic. We provide an algorithm that
ensures this by conflating epsilon-cycles within strongly connected components.
Sampling is also effective in the presence of non-injective transformations of
strings. We illustrate this in the context of decoding for Connectionist
Temporal Classification (CTC), where the predictive probabilities yield
auxiliary sequences which are transformed into shorter labeling strings. We can
sample efficiently from the transformed labeling distribution and use this in
two different strategies for finding the most probable CTC labeling
A New Kind of Finance
Finance has benefited from the Wolfram's NKS approach but it can and will
benefit even more in the future, and the gains from the influence may actually
be concentrated among practitioners who unintentionally employ those principles
as a group.Comment: 13 pages; Forthcoming in "Irreducibility and Computational
Equivalence: 10 Years After Wolfram's A New Kind of Science," Hector Zenil,
ed., Springer Verlag, 201
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