The Essence of PDP: Local Processing, Global Outcomes PDP Class January 16, 2013.

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Presentation transcript:

The Essence of PDP: Local Processing, Global Outcomes PDP Class January 16, 2013

Goodness of Network States and their Probabilities Goodness of a network state How networks maximize goodness The Hopfield network and Rumelhart’s continuous version Stochastic networks: The Boltzmann Machine, and the relationship between goodness and probability Equilibrium, ergodicity, and annealing Exploring the relationship between Goodness and Probability in an ensemble of networks

Network Goodness and How to Increase it

The Hopfield Network Assume symmetric weights. Units have binary states [+1,-1] Units are set into initial states Choose a unit to update at random If net > 0, then set state to 1. Else set state to -1. Goodness always increases… or stays the same.

Rumelhart’s Continuous Version Unit states have values between 0 and 1. Units are updated asynchronously. Update is gradual, according to the rule: There are separate scaling parameters for external and internal input:

The Cube Network Positive weights have value +1 Negative weights have value -1.5 ‘External input’ is implemented as a positive bias of.5 to all units. These values are all scaled by the istr parameter in calculating goodness in the program (istr = 0.4).

Goodness Landscape of Cube Network

Rumelhart’s Room Schema Model Units for attributes/objects found in rooms Data: lists of attributes found in rooms No room labels Weights and biases: Modes of use in simulation: –Clamp one or more units, let the network settle –Clamp all units, let the network calculate the Goodness of a state (‘pattern’ mode)

Weights for all units

Goodness Landscape for Some Rooms

Slices thru landscape with three different starting points

The Boltzmann Machine: The Stochastic Hopfield Network Units have binary states [0,1], Update is asynchronous. The activation function is: Assuming processing is ergodic: that is, it is possible to get from any state to any other state, then when the state of the network reaches equilibrium, the relative probability and relative goodness of two states are related as follows: More generally, at equilibrium we have the Probability-Goodness Equation: or

Simulated Annealing Start with high temperature. This means it is easy to jump from state to state. Gradually reduce temperature. In the limit of infinitely slow annealing, we can guarantee that the network will be in the best possible state (or in one of them, if two or more are equally good). Thus, the best possible interpretation can always be found (if you are patient)!

Exploring Probability Distributions over States Imagine settling to a non-zero temperature, such as T = 0.5. At this temperature, there’s still some probability of being in a state that is less than perfect. Consider an ensemble of networks –At equilibrium (i.e. after enough cycles, possibly with annealing) the relative frequencies of being in the different states will approximate the relative probabilities given by the Probability- Goodness equation. You will have an opportunity to explore this situation in the homework assignment