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JMLR
2010
123views more  JMLR 2010»
12 years 11 months ago
Inductive Principles for Restricted Boltzmann Machine Learning
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de ...
ICML
2010
IEEE
13 years 5 months ago
Rectified Linear Units Improve Restricted Boltzmann Machines
Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all ...
Vinod Nair, Geoffrey E. Hinton
ICML
2010
IEEE
13 years 5 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 ...
Philip M. Long, Rocco A. Servedio
FPL
2009
Springer
161views Hardware» more  FPL 2009»
13 years 9 months ago
A multi-FPGA architecture for stochastic Restricted Boltzmann Machines
Although there are many neural network FPGA architectures, there is no framework for designing large, high-performance neural networks suitable for the real world. In this paper, ...
Daniel L. Ly, Paul Chow
VIS
2004
IEEE
163views Visualization» more  VIS 2004»
14 years 6 months ago
Dispersion Simulation and Visualization For Urban Security
We present a system for simulating and visualizing the propagation of dispersive contaminants with an application to urban security. In particular, we simulate airborne contaminan...
Arie E. Kaufman, Feng Qiu, Haik Lorenz, Jianning W...