Nonlocal Transition Kernel for Efficient Learning of Restricted Boltzmann Machines
Learning restricted Boltzmann machines (RBMs) is computationally challenging because it requires expectations whose exact evaluation is generally intractable. The expectations are typically evaluated using a sampling approximation based on blocked Gibbs sampling (BGS), which is a local Markov chain Monte Carlo transition kernel. However, the locality of BGS can lead to poor sampling quality when the RBM has high energy barriers, thereby degrading learning performance. Deep tempering (DT), which performs parallel tempering over a sequence of learnable RBMs including the training RBM, alleviates this locality issue. However, DT algorithmically requires multiple steps to move through the RBM...
| Authors | Kaiji Sekimoto, Muneki Yasuda |
|---|---|
| Primary category | stat.ML |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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