NObSP: Functional Decomposition of Neural Networks via Oblique Subspace Projections

arXiv:2609.17825 · cs.LG · Submitted 2026-09-15 · Read on arXiv

cs.LG

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: 24 pages, including 6 pages of supplementary material. Code available at https://github.com/santialferez/nobsp_lib

Code: https://github.com/santialferez/nobsp_lib

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Calibration — 24× in this paper · explained in 7 episodes
• Interpretability — 23× in this paper · explained in 15 episodes
• AUC — 15× in this paper · explained in 2 episodes
• Integrated Gradients — 11× in this paper
• Embeddings — 8× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Function Reproduction Score — 5× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Shapley values — 4× in this paper · explained in 2 episodes
• Class Activation Mapping — 3× in this paper
• Partial Dependence Plots — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Accumulated Local Effects — 2× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Individual Conditional Expectation — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Annotation Errors — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model Merging — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Towards A Rigorous Science of Interpretable Machine Learning

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