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  2. Restricted Boltzmann machine - Wikipedia

    en.wikipedia.org/wiki/Restricted_Boltzmann_machine

    Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

  3. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    It uses a restricted Boltzmann machine to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly.

  4. Boltzmann machine - Wikipedia

    en.wikipedia.org/wiki/Boltzmann_machine

    In this example there are 3 hidden units (blue) and 4 visible units (white). This is not a restricted Boltzmann machine. A Boltzmann machine, like a Sherrington–Kirkpatrick model, is a network of units with a total "energy" (Hamiltonian) defined for the overall network. Its units produce binary results.

  5. Convolutional deep belief network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_deep_belief...

    [3] CDBNs use the technique of probabilistic max-pooling to reduce the dimensions in higher layers in the network. Training of the network involves a pre-training stage accomplished in a greedy layer-wise manner, similar to other deep belief networks .

  6. Deep belief network - Wikipedia

    en.wikipedia.org/wiki/Deep_belief_network

    In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.

  7. Adaptive resonance theory - Wikipedia

    en.wikipedia.org/wiki/Adaptive_resonance_theory

    Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.

  8. APBS (software) - Wikipedia

    en.wikipedia.org/wiki/APBS_(software)

    APBS (previously also Advanced Poisson-Boltzmann Solver) is a free and open-source software for solving the equations of continuum electrostatics intended primarily for the large biomolecular systems. [1] [2] It is available under the BSD license. PDB2PQR prepares the protein structure files from Protein Data Bank for use with APBS.

  9. Boltzmann sampler - Wikipedia

    en.wikipedia.org/wiki/Boltzmann_sampler

    A Boltzmann sampler is an algorithm intended for random sampling of combinatorial structures. If the object size is viewed as its energy, and the argument of the corresponding generating function is interpreted in terms of the temperature of the physical system, then a Boltzmann sampler returns an object from a classical Boltzmann distribution .