Multi-level Contextual Type Theory

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Multi-level Contextual Type Theory

Contextual type theory distinguishes between bound variables and meta-variables to write potentially incomplete terms in the presence of binders. It has found good use as a framework for concise explanations of higher-order unification, characterize holes in proofs, and in developing a foundation for programming with higher-order abstract syntax, as embodied by the programming and reasoning env...

متن کامل

Linear Contextual Modal Type Theory

When one develops, implements, and studies type theories based on linear logic, for example, in the context of theorem proving, logic programming, and formal reasoning, one is immediately confronted with questions about their equational theory and how to deal with logic variables. In this paper, we propose linear contextual modal type theory that gives a mathematical account of the nature of lo...

متن کامل

Word Recognition With Multi-Level Contextual Knowledge

A word recognition algorithm is proposed that integrates character recognition with word shape analysis. The algorithm consists of a set of serial filters and parallel classifiers, and the decisions are combined to generate a consensus ranking of the input lexicon. Experimental results with multifont machine-printed word images are discussed.

متن کامل

Explicit Substitutions for Contextual Type Theory

In this paper, we present an explicit substitution calculus which distinguishes between ordinary bound variables and meta-variables. Its typing discipline is derived from contextual modal type theory. We first present a dependently typed lambda calculus with explicit substitutions for ordinary variables and explicit meta-substitutions for meta-variables. We then present a weak head normalizatio...

متن کامل

Multi-level Contextual RNNs with Attention Model for Scene Labeling

Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored. To handle this issue, we in this work propose a novel approach for scene labeling by exploring multi-level contextual recurrent neural networks (ML-CRNNs). Speci...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Electronic Proceedings in Theoretical Computer Science

سال: 2011

ISSN: 2075-2180

DOI: 10.4204/eptcs.71.3