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ICML
2010
IEEE
13 years 8 months ago
Learning Programs: A Hierarchical Bayesian Approach
We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, ...
Percy Liang, Michael I. Jordan, Dan Klein
ICALT
2007
IEEE
13 years 9 months ago
Analyzing the Motivation of the Students in the Art Faculty for Learning Programming
In order to analyze the factors that raise the motivation of students in the art design faculty and digital design faculty to learn programming, a programming course using Process...
Yasuhiro Takemura, Hideo Nagumo, Hidekuni Tsukamot...
APIN
1999
110views more  APIN 1999»
13 years 7 months ago
The Connectionist Inductive Learning and Logic Programming System
The Connectionist Inductive Learning and Logic Programming System, C-IL 2 P, integrates the symbolic and connectionist paradigms of Artificial Intelligence through neural networks...
Artur S. d'Avila Garcez, Gerson Zaverucha
ACMSE
2010
ACM
13 years 5 months ago
Generating three binary addition algorithms using reinforcement programming
Reinforcement Programming (RP) is a new technique for automatically generating a computer program using reinforcement learning methods. This paper describes how RP learned to gene...
Spencer K. White, Tony R. Martinez, George L. Rudo...
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
13 years 6 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...