- Implemented proper continuous-time dynamics with learnable time constants
- Added decay factor based on tau parameter that controls information flow
- Multi-layer capability for deeper networks
- Self-attention mechanism for capturing temporal relationships
- Skip connections and layer normalization for better gradient flow
- Learning rate scheduling with
ReduceLROnPlateau - Early stopping to prevent overfitting
- Gradient clipping to prevent exploding gradients
- AdamW optimizer with weight decay for regularization
- Detailed metrics for both classification and regression
- Visualization of results (confusion matrices, prediction plots)
- Feature importance analysis for regression tasks
- Simple grid search to find optimal model configuration
- Best model checkpointing
- Better error handling for dataset loading
- Proper input normalization
- Support for both sequence and non-sequence data formats
The code is now much more robust, handles edge cases better, and should provide significantly better performance on both classification and regression tasks.