Tag: interpretable
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Spectral Identifiability for Interpretable Probe Geometry
Spectral Identifiability for Interpretable Probe Geometry arXiv:2511.16288v1 Announce Type: new Abstract: Linear probes are widely used to interpret and evaluate neural representations, yet their reliability remains unclear, as probes may appear accurate in some regimes but collapse unpredictably in others. We uncover a spectral mechanism behind this phenomenon and formalize it as the Spectral Identifiability…
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Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings
Non-Negative Stiefel Approximating Flow: Orthogonalish Matrix Optimization for Interpretable Embeddings arXiv:2511.06425v1 Announce Type: new Abstract: Interpretable representation learning is a central challenge in modern machine learning, particularly in high-dimensional settings such as neuroimaging, genomics, and text analysis. Current methods often struggle to balance the competing demands of interpretability and model flexibility, limiting their effectiveness in…
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Interpretable Network-assisted Random Forest+
Interpretable Network-assisted Random Forest+ arXiv:2509.15611v1 Announce Type: new Abstract: Machine learning algorithms often assume that training samples are independent. When data points are connected by a network, the induced dependency between samples is both a challenge, reducing effective sample size, and an opportunity to improve prediction by leveraging information from network neighbors. Multiple methods taking…
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ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression arXiv:2509.07108v1 Announce Type: new Abstract: Survival analysis is a fundamental tool for modeling time-to-event outcomes in healthcare. Recent advances have introduced flexible neural network approaches for improved predictive performance. However, most of these models do not provide interpretable insights into the association between exposures and…