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» The Shortcut Problem - Complexity and Approximation
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NC
1998
14 years 11 months ago
An Addition to Backpropagation for Computing Functional Roots
Many processes are composed of a n-fold repetition of some simpler process. If the whole process can be modeled with a neural network, we present a method to derive a model of the...
Lars Kindermann
93
Voted
NIPS
1998
14 years 11 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
NETWORKING
2000
14 years 11 months ago
Fairness and Aggregation: A Primal Decomposition Study
Abstract. We examine the fair allocation of capacity to a large population of best-effort connections in a typical multiple access communication system supporting some bandwidth on...
André Girard, Catherine Rosenberg, Mohammed...
NIPS
2000
14 years 11 months ago
High-temperature Expansions for Learning Models of Nonnegative Data
Recent work has exploited boundedness of data in the unsupervised learning of new types of generative model. For nonnegative data it was recently shown that the maximum-entropy ge...
Oliver B. Downs
UAI
2000
14 years 11 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman