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