Results 231 to 240 of about 185,676 (265)
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Companion of the 18th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications - OOPSLA '03, 2003
The Generative Model Transformer (GMT) project is an Open Source initiative to build a Model Driven Architecure™ tool that allows fully customisable Platform Independent Models, Platform Description Models, Texture Mappings, and Refinement Transformations.
Jorn Bettin, Ghica van Emde Boas
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The Generative Model Transformer (GMT) project is an Open Source initiative to build a Model Driven Architecure™ tool that allows fully customisable Platform Independent Models, Platform Description Models, Texture Mappings, and Refinement Transformations.
Jorn Bettin, Ghica van Emde Boas
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General models and extensionality
Journal of Symbolic Logic, 1972It is well known that equality is definable in type theory. Thus, in the language of [2], the equality relation between elements of type α is definable as , i.e., xα = yα iff every set which contains xα also contains yα. However, in a nonstandard model of type theory, the sets may be so sparse that the wff above does not denote the true equality ...
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Journal of Time Series Analysis, 1988
Abstract. Using the theory of divergent series, a class of linear filters are considered that when applied to a constant generate a wide class of deterministic trends in mean. If these filters are applied to a white noise, series are produced that have changing variances and may correspond to fractional integrated series.
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Abstract. Using the theory of divergent series, a class of linear filters are considered that when applied to a constant generate a wide class of deterministic trends in mean. If these filters are applied to a white noise, series are produced that have changing variances and may correspond to fractional integrated series.
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Mathematical Modelling of Generalization
2002This paper surveys certain developments in the use of probabilistic techniques for the modelling of generalization. Some of the main methods and key results are discussed. Many details are omitted, the aim being to give a high-level overview of the types of approaches taken and methods used.
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Generative models for molecular discovery: Recent advances and challenges
Wiley Interdisciplinary Reviews: Computational Molecular Science, 2022Klavs F. Jensen +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Chris G Willcocks, Adam Leach, Yang Long
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Chris G Willcocks, Adam Leach, Yang Long
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Model Generation System and Model Generation Method
2023KHA NGUYEN CONG, ODATE RYOSUKE
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Molecular design in drug discovery: a comprehensive review of deep generative models.
Briefings in Bioinformatics, 2021Quan Zou, Bosheng Song, Yuansheng Liu
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A comprehensive survey and analysis of generative models in machine learning
Computer Science Review, 2020Mahendra Kumar Gourisaria +2 more
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3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
ACM Transactions on Graphics, 2023Matthias Nießner +2 more
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