Learning R Programming Language With Psi Chi

Observation of psi(3770)->gamma chi(c0)

R. A. Briere, I. Brock, J. Chen, T. Ferguson, G. Tatishvili, H. Vogel, M. E. Watkins, J. L. Rosner, N. E. Adam, J. P. Alexander, K. Berkelman, D. G. Cassel, J. E. Duboscq, K. M. Ecklund, R. Ehrlich, L. Fields, R. S. Galik, L. Gibbons, R. Gray, S. W. Gray, D. L. Hartill, B. K. Heltsley, D. Hertz, C. D. Jones, J. Kandaswamy, D. L. Kreinick, V. E. Kuznetsov, H. Mahlke-Kruger, T. O. Meyer, P. U. E....

Learning Programming Language Parsing Rules

Modern programming languages have some natural language properties, such as scoping/precedence ambiguities regarding arithmetic operators (e. g. + vs. *) and word (symbol) sense ambiguities (e. g. unary vs. binary +). Typically these ambiguities are resolved according to well-known conventions so that programs do not need to contain too many parentheses. These ambiguities are not necessary in p...

Deep Reinforcement Learning for Programming Language Correction

Novice programmers often struggle with the formal syntax of programming languages. To assist them, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program ...

INEB - PSI Technical Report 2006 - 1 Neural network software tool development : exploring programming language options

Naturalizing a Programming Language via Interactive Learning

Our goal is to create a convenient natural language interface for performing wellspecified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow user...