Sciweavers

Share
ICML
2010
IEEE

Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate

11 years 10 months ago
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
Restricted Boltzmann Machines (RBMs) are a type of probability model over the Boolean cube {-1, 1}n that have recently received much attention. We establish the intractability of two basic computational tasks involving RBMs, even if only a coarse approximation to the correct output is required. We first show that assuming P = NP, for any fixed positive constant K (which may be arbitrarily large) there is no polynomial-time algorithm for the following problem: given an n-bit input string x and the parameters of a RBM M, output an estimate of the probability assigned to x by M that is accurate to within a multiplicative factor of eKn . This hardness result holds even if the parameters of M are constrained to be at most (n) for any function (n) that grows faster than linearly, and if the number of hidden nodes of M is at most n. We then show that assuming RP = NP, there is no polynomial-time randomized algorithm for the following problem: given the parameters of an RBM M, generate a rand...
Philip M. Long, Rocco A. Servedio
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Philip M. Long, Rocco A. Servedio
Comments (0)
books