- End-to-End cancer detection pipelines for biopsy tissue samples
- Low Rank, Fast and memory efficient Denoising Algorithms with error bounds
- Higher Order feature selection algorithms to weed out redundant and irrelevant features
- Two stage neural engine as alternative to randomized SVD techniques
- Explicit Memory requirement: guided by feature dimension and desired rank
- Fully interpretable model: all outputs and weights have specific meaning
- Group-fairness loss function based on Accuracy Parity measure
- Balanced group accuracy around Target-group detection
- Group disparity reduced from ~22% to ~8% with minimal accuracy drop
- Conditional MTL model to learn toxicity targeted at different groups
- Improved Recall ~8% and ~15% over Independent and SoA MTL models
- Runtime and Parameter reductions by ~56% and ~72% over Baseline