Memorization is worst-case generalization. Based on MacKay's information theoretic model of supervised machine learning, this article discusses how to practically estimate the maximum size of a neural network given a training data set. First, we present four easily applicable rules to analytically determine the capacity of neural network architectures. This allows the comparison of the efficiency of different network architectures independently of a task. Second, we introduce and experimentally validate a heuristic method to estimate the neural network capacity requirement for a given dataset and labeling. This allows an estimate of the required size of a neural network for a given problem. We conclude the article with a discussion on the consequences of sizing the network wrongly, which includes both increased computation effort for training as well as reduced generalization capability.
Even though perceptrons have been utilized for a long time, its capacities have been rarely explored beyond discussion of linear separability. Moreover, catastrophic forgetting has so far not been explained satisfactorily. Catastrophic forgetting @cite_37 @cite_28 is a phenomenon consisting in the very quick loss of the network's capability to classify the first set of labels, when the net is first trained on one set of labels and then on another set of labels. Our interpretation of the cause of this phenomenon is that it is simply a capacity overflow.
@cite_28 @cite_37
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Multilayer connectionist models of memory based on the encoder model using the backpropagation learning rule are evaluated. The models are applied to standard recognition memory procedures in which items are studied sequentially and then tested for retention. Sequential learning in these models leads to 2 major problems. First, well-learned information is forgotten rapidly as new information is learned. Second, discrimination between studied items and new items either decreases or is nonmonotonic as a function of learning. To address these problems, manipulations of the network within the multilayer model and several variants of the multilayer model were examined, including a model with prelearned memory and a context model, but none solved the problems. The problems discussed provide limitations on connectionist models applied to human memory and in tasks where information to be learned is not all available during learning. The first stage of the connectionist revolution in psychology is reaching maturity and perhaps drawing to an end. This stage has been concerned with the exploration of classes of models, and the criteria that have been used to evaluate the success of an application have been necessarily loose. In the early stages of development of a new approach, lax acceptability criteria are appropriate because of the large range of models to be examined. However, there comes a second stage when the models serve as competitors to existing models developed within other theoretical frameworks, and they have to be competitively evaluated according to more stringent criteria. A few notable connectionist models have reached these standards, whereas others have not. The second stage of development also requires that the connectionist models be evaluated in areas where their potential for success is not immediately obvious. One such area is recognition memory. The work presented in this article evaluates several variants of the multilayer connectionist model as accounts of empirical results in this area. I mainly discuss multilayer models using the error-correcting backpropagation algorithm and do not address other architectures such as adaptive resonance schemes (Carpenter & Grossberg, 1987). Before launching into the modeling of recognition memory, I need to specify the aims and rules under which this project was carried out. This is important in a new area of inquiry because there are many divergent views about what needs to be Publisher Summary Connectionist networks in which information is stored in weights on connections among simple processing units have attracted considerable interest in cognitive science. Much of the interest centers around two characteristics of these networks. First, the weights on connections between units need not be prewired by the model builder but rather may be established through training in which items to be learned are presented repeatedly to the network and the connection weights are adjusted in small increments according to a learning algorithm. Second, the networks may represent information in a distributed fashion. This chapter discusses the catastrophic interference in connectionist networks. Distributed representations established through the application of learning algorithms have several properties that are claimed to be desirable from the standpoint of modeling human cognition. These properties include content-addressable memory and so-called automatic generalization in which a network trained on a set of items responds correctly to other untrained items within the same domain. New learning may interfere catastrophically with old learning when networks are trained sequentially. The analysis of the causes of interference implies that at least some interference will occur whenever new learning may alter weights involved in representing old learning, and the simulation results demonstrate only that interference is catastrophic in some specific networks.