Helmholtz machine
•Download as PPTX, PDF•
0 likes•159 views
A stochastic Helmholtz machine uses a neural network with bottom-up and top-down connections between layers to convert input vectors into representations in hidden units and then reconstruct approximations of the input from those representations. It contains two connected models that allow information exchange between layers, with all neurons being stochastic binary units that can be in one of two states. The machine is usually trained using an unsupervised learning algorithm like wake-sleep that involves alternating between sleep and wake phases.
Recommended
Recommended
More Related Content
Recently uploaded
Recently uploaded (20)
Featured
Featured (20)
Helmholtz machine
- 2. The neural network has two quite different sets of connections. The bottom-up “recognition” connections are used to convert the input vector into a representation in one or more layers of hidden units. The top-down “generative” connections are then used to reconstruct an approximation to the input vector from its underlying representation. Helmholtz machines are usually trained using an unsupervised learning algorithm, such as the wake-sleep algorithm.
- 4. STOCHASTIC HELMHOLTZ MACHINE Two models connected as a circular loop allow information exchange between layers.
- 5. All neurons are stochastic binary neurons containing two states, 0 or 1.
- 6. Sleep Phase
Editor's Notes
- This special issue focuses on four important questions about neural computation: How is the world represented in the firing of neurons? What are the functional roles of bottom-up and top-down connections between cortical areas? Does the brain use internal models of the external world? What are the basic synaptic plasticity rules? We believe that answers to these questions are indicated by the new theory of unsupervised learning that we present here. Amygdala: almond-shaped neural structure in the anterior part of the temporal lobe of the cerebrum, it plays an important role in motivation and emotional behavior
- Developed by Hermann Ludwig Ferdinand von Helmholtz (German Physician)
- A simple three layer Helmholtz machine modeling the activity of 5 binary inputs (layer 1) using a two-stage hierarchical model. Generative weights (0) are shown as dashed lines, including the generative biases, the only such input to the units in the top layer. Recognition weights ($) are shown with solid lines. The recognition model performs the coding operation of Turning inputs d into stochastic codes in the hidden layer; the generative model reconstructs its best guess of the input on the basis of the code that it sees.
- The original SHM is a three layer model; however, it is replaced by a two layer model (as shown in Figure 2) in order to advance the implementation of the neural architecture. The first layer (A) is the input layer and the second layer is the output layer (B). There are two separated connection models: a recognition model and a generative model. A recognition model refers to the recognition weights (R ab ) that connect layer A to layer B. A generative model refers to the generative weights (G ba ) that connect layer B back to layer A.
- P is Probability.
- The learning method for the stochastic HM is called the wake-sleep algorithm. During the wake phase, samples are taken from the recognition model, and the parameters? of the generative model are updated according to the sampled gradient of F. In step 1, the input data is entered into the input layer (A). The recognition model then calculates a probability distribution over the neurons P R (s b ) at the output layer (B), where the probability of each neuron P(s b =1) can be calculated using equation 1. During the sleep phase, samples (fantasies) are generated top-down by the generative model, and the parameters ? of the recognition model are updated to make it better ap-proximate the inverse of the generative model.