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Wolfram Menzel

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3 papers
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3

I&C Journal 2004 Journal Article

Classes with easily learnable subclasses

  • Sanjay Jain
  • Wolfram Menzel
  • Frank Stephan

In this paper we study the question of whether identifiable classes have subclasses which are identifiable under a more restrictive criterion. The chosen framework is inductive inference, in particular the criterion of explanatory learning (Ex) of recursive functions as introduced by Gold [Inform. Comput. 10 (1967) 447]. Among the more restrictive criteria is finite learning where the learner outputs, on every function to be learned, exactly one hypothesis (which has to be correct). The topic of the present paper are the natural variants (a) and (b) below of the classical question whether a given learning criterion like finite learning is more restrictive than Ex-learning. (a) Does every infinite Ex-identifiable class have an infinite finitely identifiable subclass? (b) If an infinite Ex-identifiable class S has an infinite finitely identifiable subclass, does it necessarily follow that some appropriate learner Ex-identifies S as well as finitely identifies an infinite subclass of S? These questions are also treated in the context of ordinal mind change bounds.

JELIA Conference 2000 Invited Paper

The KeY Approach: Integrating Object Oriented Design and Formal Verification

  • Wolfgang Ahrendt
  • Thomas Baar
  • Bernhard Beckert
  • Martin Giese
  • Elmar Habermalz
  • Reiner Hähnle
  • Wolfram Menzel
  • Peter H. Schmitt

Abstract This paper reports on the ongoing KeY project aimed at bridging the gap between (a) object-oriented software engineering methods and tools and (b) deductive verification. A distinctive feature of our approach is the use of a commercial CASE tool enhanced with functionality for formal specification and deductive verification.

NeurIPS Conference 1991 Conference Paper

HARMONET: A Neural Net for Harmonizing Chorales in the Style of J. S. Bach

  • Hermann Hild
  • Johannes Feulner
  • Wolfram Menzel

HARMONET, a system employing connectionist networks for music pro(cid: 173) cessing, is presented. After being trained on some dozen Bach chorales using error backpropagation, the system is capable of producing four-part chorales in the style of J. s. Bach, given a one-part melody. Our system solves a musical real-world problem on a performance level appropriate for musical practice. HARMONET's power is based on (a) a new coding scheme capturing musically relevant information and (b) the integration of backpropagation and symbolic algorithms in a hierarchical system, com(cid: 173) bining the advantages of both.

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