15 search results - page 1 / 3 » Annealing stochastic approximation Monte Carlo algorithm for... |
140
Voted
ML
2007
ACM
15 years 19 days ago
2007
ACM
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
98
Voted
ASPDAC
2000
ACM
15 years 5 months ago
2000
ACM
Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD field in the past two decades for handling NP-hard optimization problems...
122
Voted
ICML
1999
IEEE
16 years 1 months ago
1999
IEEE
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
99
Voted
CEC
2007
IEEE
15 years 7 months ago
2007
IEEE
— This paper presents a single-objective and a multiobjective stochastic optimization algorithms for global training of neural networks based on simulated annealing. The algorith...
106
Voted
NIPS
1998
15 years 2 months ago
1998
We propose a novel strategy for training neural networks using sequential Monte Carlo algorithms. This global optimisation strategy allows us to learn the probability distribution...