Networks that learn, and the networks they learn

Optimization for Problem Classes – Neural Networks that Learn to Learn –

The main focus of the optimization of artificial neural networks has been the design of a problem dependent network structure in order to reduce the model complexity and to minimize the model error. Driven by a concrete application we identify in this paper another desirable property of neural networks – the ability of the network to efficiently solve related problems denoted as a class of prob...

GAL: Networks That Grow When They Learn and Shrink When They Forget

Learning when limited to modiication of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artiicial neural networks, learning by iterative adjustment of synaptic weights can only succeed if the network designer predeenes an appropriate network structure, i.e., number of hidden layers, units, an...

Parallel Distributed Belief Networks That Learn

A parallel distributed computational model for reasoning and learning is discussed based on a belief network paradigm. Issues like reasoning and learning for the proposed model are discussed. Comparisons between our method and other methods are also given.

Networks That Learn for Image Understanding

Learning is becoming a central problem in trying to understand intelligence and in trying to develop intelligent machines. This paper describes some recent work on developing machines that learn in the domains of vision and graphics. We will introduce an underlying theory which connects function approximation techniques, neural network architectures and statistical methods. While these techniqu...

Recurrent Networks That Learn Nonregular Languages