The JStar language philosophy

This paper introduces the JStar parallel programming language, which is a Java-based declarative language aimed at discouraging sequential programming, en-couraging massively parallel programming, and giving the compiler and runtime maximum freedom to try alternative parallelisation strategies. We describe the execution semantics and runtime support of the language, several optimisations and parallelism strategies, with some benchmark results

Cognitive apprenticeship : teaching the craft of reading, writing, and mathtematics

Includes bibliographical references (p. 25-27)This research was supported by the National Institute of Education under Contract no. US-NIE-C-400-81-0030 and the Office of Naval Research under Contract No. N00014-85-C-002

Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201