Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks
This work addresses the challenge of architecture selection and initialization for neural networks, offering a potentially incremental improvement for researchers and practitioners in machine learning.
The authors tackled the problem of constructing efficient single hidden layer neural networks by introducing a greedy algorithm that uses a reduced dictionary via ridgelet transform, resulting in networks that can serve as improved initializations or fully-trained alternatives to backpropagation, with numerical experiments showing advantages over conventional methods.
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function. In our approach we obtain a shallow network by utilizing a greedy algorithm with the prescribed dictionary provided by the available training data and a set of possible inner weights. To facilitate the greedy selection process we employ an integral representation of the network, based on the ridgelet transform, that significantly reduces the cardinality of the dictionary and hence promotes feasibility of the greedy selection. Our approach allows for the construction of efficient architectures which can be treated either as improved initializations to be used in place of random-based alternatives, or as fully-trained networks in certain cases, thus potentially nullifying the need for backpropagation training. Numerical experiments demonstrate the tenability of the proposed concept and its advantages compared to the conventional techniques for selecting architectures and initializations for neural networks.