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» Functional Differentiation of Computer Programs
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CIG
2005
IEEE
15 years 8 months ago
Nannon: A Nano Backgammon for Machine Learning Research
A newly designed game is introduced, which feels like Backgammon, but has a simplified rule set. Unlike earlier attempts at simplifying the game, Nannon maintains enough features a...
Jordan B. Pollack
106
Voted
AI
2001
Springer
15 years 6 months ago
Learning about Constraints by Reflection
A system's constraints characterizes what that system can do. However, a dynamic environment may require that a system alter its constraints. If feedback about a specific situ...
J. William Murdock, Ashok K. Goel
104
Voted
CORR
2010
Springer
110views Education» more  CORR 2010»
15 years 2 months ago
Advanced Software Protection Now
We introduce a novel software-protection method, which can be fully implemented with today's technologies, that provides traitor tracing and license-enforcement functionalitie...
Diego Bendersky, Ariel Futoransky, Luciano Notarfr...
ICALP
2007
Springer
15 years 8 months ago
Unbounded-Error One-Way Classical and Quantum Communication Complexity
This paper studies the gap between classical one-way communication complexity C(f) and its quantum counterpart Q(f), under the unbounded-error setting, i.e., it is enough that the ...
Kazuo Iwama, Harumichi Nishimura, Rudy Raymond, Sh...
114
Voted
IJCNN
2006
IEEE
15 years 8 months ago
On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning
— We present a simple, intuitive argument based on “invariant imbedding” in the spirit of dynamic programming to derive a stagewise second-order backpropagation (BP) algorith...
Eiji Mizutani, Stuart Dreyfus