Next Generation of Neural networks

1. What is wrong with back-propagation

• It requires labeled training data. -Almost data is unlabeled
• Unless the weights are highly redundant, labels cannot possibly provide enough information
• The learning time does not scale well. -It’s very slow in networks with multiple hidden layers
• The neurons need to send two different types of signal (Forward pass: activation = a | Backward pass: δ=∂Cost∂z )

2. How to overcome the limitations of back-propagation

One promising approach is that we need to keep the efficiency of using a gradient method for adjusting the weights, but also use it for modeling the structure of the sensory input at the same time.

Adjust the weights to maximize the probability that a generative model would have produced the sensory input. Try to learn p(image) not p(label | image)

Weights → Energies → Probabilites

Each possible joint configuration of the visible and hidden units has a Hopfield “energy”. The energy is determined by the weights and biases.

The energy of a joint configuration of the visible and

hidden units determines the probability that the network

will choose that configuration.

−−RestrcitedBoltzmannMachine