Learning Infinite RBMs with Frank-Wolfe

Nov-21-2025, 10:23:08 GMT–Neural Information Processing Systems

One practical problem when using a RBM is that we need to decide the size of the hidden layer (number of hidden units) before performing learning, and it can be challenging to decide what is the optimal size. One simple heuristic is to search the'best" number of hidden units using cross

artificial intelligence, bayesian inference, machine learning, (20 more...)

Neural Information Processing Systems

Nov-21-2025, 10:23:08 GMT

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Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning
    - Learning Graphical Models > Directed Networks
      - Bayesian Learning (0.68)
    - Neural Networks > Deep Learning (0.70)
    - Statistical Learning (1.00)
  - Representation & Reasoning > Uncertainty
    - Bayesian Inference (0.46)

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