hamiltonian monte carlo
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
When the activation function of a network is analytic (e.g., sigmoid
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Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang, Jun WANG, Zhanxing Zhu
Neural Information Processing Systems http://nips.cc/
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
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Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
Oren Mangoubi, Nisheeth Vishnoi
Neural Information Processing Systems http://nips.cc/
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The Randomized Midpoint Method for Log-Concave Sampling
Neural Information Processing Systems http://nips.cc/
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
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- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.68)
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Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou, Pan Xu, Quanquan Gu
Figure 3: Ex represents (c)-(d) ConB.
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Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space
Our algorithm incorporates constraints into the Riemannian version of Hamiltonian Monte Carlo and maintains sparsity.
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- Asia > Middle East > Israel (0.04)
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