SCBI: Stochastic Covariance-Based Initialization

A novel neural network weight initialization method achieving 87× faster convergence on regression tasks and 33% lower initial loss on classification tasks compared to standard Xavier/He initialization. SCBI (Stochastic Covariance-Based Initialization) leverages stochastic bagging and ridge-regularized Normal Equation solving to provide data-driven initialization that places weights near the optimal solution before training begins. This eliminates the "cold start" problem in high-dimensional regression and classification tasks. Key Features:- 87× faster convergence on regression- 33% improvement on classification initial loss- Zero hyperparameter tuning required- Universal: works for regression and classification- GPU-accelerated implementation in PyTorch This package includes:- Complete implementation (scbi.py)- Research paper (scbi_paper.pdf)- Working examples and benchmarks- Full documentation and quick start guide- MIT licensed The method is particularly effective for:- Tabular data with high-dimensional features- First layer initialization in deep networks- Classification heads in transfer learning- Linear and logistic regression models