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STOC
1993
ACM
141views Algorithms» more  STOC 1993»
13 years 11 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
FOCS
1989
IEEE
13 years 11 months ago
Constant Depth Circuits, Fourier Transform, and Learnability
In this paper, Boolean functions in ,4C0 are studied using harmonic analysis on the cube. The main result is that an ACO Boolean function has almost all of its “power spectrum”...
Nathan Linial, Yishay Mansour, Noam Nisan
AAAI
2010
13 years 8 months ago
Multi-Agent Plan Recognition: Formalization and Algorithms
Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents...
Bikramjit Banerjee, Landon Kraemer, Jeremy Lyle
IPCO
2010
125views Optimization» more  IPCO 2010»
13 years 8 months ago
A Pumping Algorithm for Ergodic Stochastic Mean Payoff Games with Perfect Information
Abstract. We consider two-person zero-sum stochastic mean payoff games with perfect information, or BWR-games, given by a digraph G = (V = VB VW VR, E), with local rewards r : E R...
Endre Boros, Khaled M. Elbassioni, Vladimir Gurvic...
RSS
2007
136views Robotics» more  RSS 2007»
13 years 8 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...