SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.
Front-end of line and middle-of-line time-dependent dielectric breakdown reliability simulator for logic circuits
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
SDAMI detects interactions in high-dimensional data via an Effect Footprint principle and models them using sparsity, group lasso, and dedicated deep subnetworks for improved interpretability.