Sciweavers
Explore
Publications
Books
Software
Tutorials
Presentations
Lectures Notes
Datasets
Labs
Conferences
Community
Upcoming
Conferences
Top Ranked Papers
Most Viewed Conferences
Conferences by Acronym
Conferences by Subject
Conferences by Year
Tools
PDF Tools
Image Tools
Text Tools
OCR Tools
Symbol and Emoji Tools
On-screen Keyboard
Latex Math Equation to Image
Smart IPA Phonetic Keyboard
Community
Sciweavers
About
Terms of Use
Privacy Policy
Cookies
Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools
98
click to vote
CIE
2006
Springer
82
views
Applied Computing
»
more
CIE 2006
»
Deep Inference and Its Normal Form of Derivations
15 years 6 months ago
Download
www.iam.unibe.ch
Abstract. We see a notion of normal derivation for the calculus of structures, which is based on a factorisation of derivations and which is more general than the traditional notion of cut-free proof in this formalism.
Kai Brünnler
Real-time Traffic
Applied Computing
|
CIE 2006
|
Cut-free Proof
|
Normal Derivation
|
Traditional Notion
|
claim paper
Related Content
»
Deep Sequent Systems for Modal Logic
»
Finitesample inference with monotone incomplete multivariate normal data II
»
An Accurate ClosedForm Approximation of the Distributed MIMO Outage Probability
»
Information Extraction as an Ontology Population Task and Its Application to Genic Interac...
»
Learning Deep Web Crawling with Diverse Features
»
Analyzing MCSF dependent monocytemacrophage differentiation Expression modes and metamodes...
»
Operations for inference in continuous Bayesian networks with linear deterministic variabl...
»
Algorithms to Distinguish the Role of GeneConversion from SingleCrossover Recombination in...
»
Efficient solutions to the braid isotopy problem
more »
Post Info
More Details (n/a)
Added
20 Aug 2010
Updated
20 Aug 2010
Type
Conference
Year
2006
Where
CIE
Authors
Kai Brünnler
Comments
(0)
Researcher Info
Applied Computing Study Group
Computer Vision