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» Generating from a Deep Structure
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152
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SSPR
2010
Springer
15 years 1 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
ICSE
2007
IEEE-ACM
16 years 3 months ago
Feedback-Directed Random Test Generation
We present a technique that improves random test generation by incorporating feedback obtained from executing test inputs as they are created. Our technique builds inputs incremen...
Carlos Pacheco, Shuvendu K. Lahiri, Michael D. Ern...
114
Voted
INFOCOM
2009
IEEE
15 years 10 months ago
An Economically-Principled Generative Model of AS Graph Connectivity
We explore the problem of modeling Internet connectivity at the Autonomous System (AS) level and present an economically-principled dynamic model that reproduces key features of t...
Jacomo Corbo, Shaili Jain, Michael Mitzenmacher, D...
150
Voted
EMSOFT
2010
Springer
15 years 1 months ago
From high-level component-based models to distributed implementations
Constructing correct distributed systems from their high-level models has always been a challenge and often subject to serious errors because of their non-deterministic and non-at...
Borzoo Bonakdarpour, Marius Bozga, Mohamad Jaber, ...
127
Voted
POPL
2010
ACM
16 years 23 days ago
Dependent Types from Counterexamples
d by recent research in abstract model checking, we present a new approach to inferring dependent types. Unlike many of the existing approaches, our approach does not rely on prog...
Tachio Terauchi