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Electronic Colloquium on Computational Complexity

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TR04-033 | 23rd January 2004
Michael Schmitt

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions

We study networks of spiking neurons that use the timing of pulses
to encode information. Nonlinear interactions model the spatial
groupings of synapses on the dendrites and describe the computations
performed at local branches. We analyze the question of how many
examples these networks must ... more >>>


TR04-032 | 5th February 2004
Ryan Williams

A new algorithm for optimal constraint satisfaction and its implications

We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX-2-CSP and MIN-2-CSP), which gives the first exponential improvement over the trivial algorithm; more precisely, it is a constant factor improvement in the base of ... more >>>


TR04-031 | 22nd March 2004
Troy Lee, Andrei Romashchenko

On Polynomially Time Bounded Symmetry of Information

The information contained in a string $x$ about a string $y$
is defined as the difference between the Kolmogorov complexity
of $y$ and the conditional Kolmogorov complexity of $y$ given $x$,
i.e., $I(x:y)=\C(y)-\C(y|x)$. From the well-known Kolmogorov--Levin Theorem it follows that $I(x:y)$ is symmetric up to a small ... more >>>



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