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Building fast Bayesian computing machines out of intentionally
  stochastic, digital parts

Building fast Bayesian computing machines out of intentionally stochastic, digital parts

20 February 2014
Vikash K. Mansinghka
Eric Jonas
ArXiv (abs)PDFHTML

Papers citing "Building fast Bayesian computing machines out of intentionally stochastic, digital parts"

4 / 4 papers shown
Learning and Inference in Sparse Coding Models with Langevin Dynamics
Learning and Inference in Sparse Coding Models with Langevin DynamicsNeural Computation (Neural Comput.), 2022
Michael Y.-S. Fang
M. Mudigonda
Ryan Zarcone
A. Khosrowshahi
Bruno A. Olshausen
SyDa
284
6
0
23 Apr 2022
Beyond Application End-Point Results: Quantifying Statistical Robustness
  of MCMC Accelerators
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Xinming Zhang
Ramin Bashizade
Yicheng Wang
Cheng Lyu
S. Mukherjee
A. Lebeck
303
5
0
05 Mar 2020
Optimal Approximate Sampling from Discrete Probability Distributions
Optimal Approximate Sampling from Discrete Probability Distributions
Feras A. Saad
Cameron E. Freer
Martin Rinard
Vikash K. Mansinghka
300
9
0
13 Jan 2020
Learning Machines Implemented on Non-Deterministic Hardware
Learning Machines Implemented on Non-Deterministic Hardware
Suyog Gupta
Vikas Sindhwani
K. Gopalakrishnan
203
2
0
09 Sep 2014
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