Bayesian optimization for sparse neural networks with trainable activation functions
This work addresses the problem of overfitting and convergence time in deep learning for researchers and practitioners, but it is incremental as it builds on existing trainable activation function methods.
The paper tackles the problem of enhancing neural network performance by proposing a trainable activation function with parameters estimated via a fully Bayesian model and MCMC-based optimization, tested on three datasets with CNNs and showing improved model accuracy.
In the literature on deep neural networks, there is considerable interest in developing activation functions that can enhance neural network performance. In recent years, there has been renewed scientific interest in proposing activation functions that can be trained throughout the learning process, as they appear to improve network performance, especially by reducing overfitting. In this paper, we propose a trainable activation function whose parameters need to be estimated. A fully Bayesian model is developed to automatically estimate from the learning data both the model weights and activation function parameters. An MCMC-based optimization scheme is developed to build the inference. The proposed method aims to solve the aforementioned problems and improve convergence time by using an efficient sampling scheme that guarantees convergence to the global maximum. The proposed scheme is tested on three datasets with three different CNNs. Promising results demonstrate the usefulness of our proposed approach in improving model accuracy due to the proposed activation function and Bayesian estimation of the parameters.