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Jürgen Schmidhuber - Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs

Machine Learning Street Talk (MLST)

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Simplicity Over Complexity in Neural Network Design

Maximizing the effectiveness of neural networks requires focusing on simplicity rather than the number of hidden units. While a single layer with enough hidden units can theoretically approximate any piecewise continuous function, this complexity often leads to an abundance of weights, which hampers the network's ability to generalize. The goal should be to identify the simplest neural network capable of solving a given problem. In many instances, particularly in temporal processing, the most effective solutions are found in deep, yet simple architectures, such as recurrent neural networks, which efficiently utilize depth to capture and process information that may be relevant much later in the sequence.

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