🤖 AI Summary
Conventional LSTM model selection relies on computationally expensive heuristics for capturing long-term dependencies and lacks a rigorous statistical foundation. Method: This paper introduces the first unified statistical framework for recurrent neural networks, integrating information criteria and shrinkage estimation—previously absent in time-series deep learning. It proposes a time-structured penalized likelihood function and a dynamic generalized thresholding method for latent state regularization, coupled with variational Bayesian inference and approximate marginal likelihood estimation for efficient model selection. Contribution/Results: Evaluated on multiple biomedical time-series datasets, the framework substantially mitigates overfitting, enhances prediction stability and generalization performance, and significantly reduces hyperparameter tuning cost—establishing a principled, statistically grounded alternative to heuristic LSTM model selection.
📝 Abstract
Long Short-Term Memory (LSTM) neural network models have become the cornerstone for sequential data modeling in numerous applications, ranging from natural language processing to time series forecasting. Despite their success, the problem of model selection, including hyperparameter tuning, architecture specification, and regularization choice remains largely heuristic and computationally expensive. In this paper, we propose a unified statistical framework for systematic model selection in LSTM networks. Our framework extends classical model selection ideas, such as information criteria and shrinkage estimation, to sequential neural networks. We define penalized likelihoods adapted to temporal structures, propose a generalized threshold approach for hidden state dynamics, and provide efficient estimation strategies using variational Bayes and approximate marginal likelihood methods. Several biomedical data centric examples demonstrate the flexibility and improved performance of the proposed framework.