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Current methods for estimating the required neural-network size for a givenproblem class have focused on methods that can be computationally intensive,such as neural-architecture search and pruning. In contrast, methods that addcapacity to neural networks as needed may provide similar results toarchitecture search and pruning, but do not require as much computation to findan appropriate network size. Here, we present a network-growth method thatsearches for explainable error in the network's residuals and grows the networkif sufficient error is detected. We demonstrate this method using examples fromclassification, imitation learning, and reinforcement learning. Within thesetasks, the growing network can often achieve better performance than smallnetworks that do not grow, and similar performance to networks that begin muchlarger.

Adaptive Neural Networks Using Residual Fitting