Training neural networks to encode symbols enables combinatorial generalization

Generalization Capacity of Handwritten Outlier Symbols Rejection with Neural Network

Different problems of generalization of outlier rejection exist depending of the context. In this study we firstly define three different problems depending of the outlier availability during the learning phase of the classifier. Then we propose different solutions to reject outliers with two main strategies: add a rejection class to the classifier or delimit its knowledge to better reject what...

Generalization by Neural Networks

Neural networks have traditionally been applied to recognition problems, and most learning algorithms are tailored to those problems. We discuss the requirements of learning for generalization, where the traditional methods based on gradient descent have limited success. We present a new stochastic learning algorithm based on simulated annealing in weight space. We verify the convergence proper...

Grounding Predicate Symbols with Neural Networks

We propose a cognitive architecture which integrates symbolic and connectionist representations. The symbolic representations, in the form of predicate logic, emerge from, and are grounded in, nonsymbolic experience. This leads to multi-level representations, both symbolic and non-symbolic. This architecture is preliminary and is used as motivation for the methods described for grounding predic...

On Generalization by Neural Networks

We report new results on the corner classification approach to training feedforward neural networks. It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization. The paper also deals with the relations between the number of separable regions and the size of the training set for a binary data network. Prescri...

Recognition of Handwritten Persian Numbers and Arithmetic Symbols Using Neural Networks