Topic
Low-level programming language
About: Low-level programming language is a research topic. Over the lifetime, 2320 publications have been published within this topic receiving 53840 citations. The topic is also known as: low-level language & machine-oriented language.
Papers published on a yearly basis
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Papers
TL;DR: It was found that theclass of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learningable from a text.
Abstract: Language learnability has been investigated. This refers to the following situation: A class of possible languages is specified, together with a method of presenting information to the learner about an unknown language, which is to be chosen from the class. The question is now asked, “Is the information sufficient to determine which of the possible languages is the unknown language?” Many definitions of learnability are possible, but only the following is considered here: Time is quantized and has a finite starting time. At each time the learner receives a unit of information and is to make a guess as to the identity of the unknown language on the basis of the information received so far. This process continues forever. The class of languages will be considered learnable with respect to the specified method of information presentation if there is an algorithm that the learner can use to make his guesses, the algorithm having the following property: Given any language of the class, there is some finite time after which the guesses will all be the same and they will be correct. In this preliminary investigation, a language is taken to be a set of strings on some finite alphabet. The alphabet is the same for all languages of the class. Several variations of each of the following two basic methods of information presentation are investigated: A text for a language generates the strings of the language in any order such that every string of the language occurs at least once. An informant for a language tells whether a string is in the language, and chooses the strings in some order such that every string occurs at least once. It was found that the class of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learnable from a text.
3,876 citations
Book•
IBM1
TL;DR: The paper describes a succinct problem-oriented programming language that relies heavily on a systematic extension of a small set of basic operations to vectors, matrices, and trees, and on a family of flexible selection operations controlled by logical vectors.
Abstract: The paper describes a succinct problem-oriented programming language. The language is broad in scope, having been developed for, and applied effectively in, such diverse areas as microprogramming, switching theory, operations research, information retrieval, sorting theory, structure of compilers, search procedures, and language translation. The language permits a high degree of useful formalism. It relies heavily on a systematic extension of a small set of basic operations to vectors, matrices, and trees, and on a family of flexible selection operations controlled by logical vectors. Illustrations are drawn from a variety of applications.
1,178 citations
Proceedings Article•
11 Jul 2010TL;DR: This work addresses the problem of selecting non-domain-specific language model training data to build auxiliary language models for use in tasks such as machine translation by comparing the cross-entropy, according to domain-specific and non- domain-specifc language models, for each sentence of the text source used to produce the latter language model.
Abstract: We address the problem of selecting non-domain-specific language model training data to build auxiliary language models for use in tasks such as machine translation. Our approach is based on comparing the cross-entropy, according to domain-specific and non-domain-specifc language models, for each sentence of the text source used to produce the latter language model. We show that this produces better language models, trained on less data, than both random data selection and two other previously proposed methods.
703 citations
TL;DR: A simple but effective rote-learning facility can be provided within the framework of a suitable programming language to improve the efficiency of computer programs during execution.
Abstract: It would be useful if computers could learn from experience and thus automatically improve the efficiency of their own programs during execution A simple but effective rote-learning facility can be provided within the framework of a suitable programming language
609 citations
Book•
1 Apr 2010
TL;DR: The report gives a defining description of the programming language Scheme, a statically scoped and properly tail-recursive dialect of the Lisp programming language invented by Guy Lewis Steele, Jr. and Gerald Jay Sussman.
Abstract: Programming languages should be designed not by piling feature on top of feature, but by removing the weaknesses and restrictions that make additional features appear necessary. Scheme demonstrates that a very small number of rules for forming expressions, with no restrictions on how they are composed, are enough to form a practical and efficient programming language that is flexible enough to support most of the major programming paradigms in use today. This book contains the three parts comprising 'R6RS', the sixth revision of a series of reports describing the programming language Scheme. The book is divided into parts: a description of the language itself, a description of the standard libraries and non-normative appendices. Early chapters introduce Scheme and later chapters act as a reference manual. This is an important report for programmers that work with or want to learn about the Scheme language.
576 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 1 |
| 2023 | 1 |
| 2022 | 4 |
| 2021 | 5 |
| 2020 | 6 |
| 2019 | 4 |