aiqbench analysis
This arXiv preprint proposes a neural architecture that periodically clusters SHAP feature-importance profiles during training and embeds the resulting subgroup identities into the network, so predictions can adapt to each observation's inferred latent structure. It extends the authors' earlier FORCE method, which reported gains from clustering on importance patterns rather than raw features. The performance claims are unverified here, so readers should check the benchmarks, baselines, and computational overhead of repeated SHAP clustering before drawing conclusions.
Joint Embedding with Dynamic Integration network (JEDI-net)
arXiv:2610.17314v1 Announce Type: cross Abstract: Deep learning has become the dominant form of machine learning due in part, to its ability to automatically engineer complex meta features from data. However, even deep learning features can perform erratically because of unobserved latent factors. incorporating latent factor information explicitly, can improve performance by letting the model alter its behavior based on such inferred factors. Because neural networks often capture such complexity inherently, external clustering of the original feature space offers limited benefit. clustering observations by feature importance profiles, rather than raw features, offers a principled way to uncover latent structure, since observations with similar importance patterns may share predictive dynamics. In a prior work, Feature Oriented Representation with Clustering and Explanation (FORCE) we showed that this approach measurably improved neural network performance. In this article, we present JEDI-net(Joint Embedding with Dynamic Integration Network) a unified architecture that periodically clusters SHAP values during optimization to identify latent subgroups with similar feature importance patterns, embed their identities directly into the network, allowing predictions to adapt to each observations predictive structure.
Abstract:Deep learning has become the dominant form of machine learning due in part, to its ability to automatically engineer complex meta features from data. However, even deep learning features can perform erratically because of unobserved latent factors. incorporating latent factor information explicitly, can improve performance by letting the model alter its behavior based on such inferred factors. Because neural networks often capture such complexity inherently, external clustering of the original feature space offers limited benefit. clustering observations by feature importance profiles, rather than raw features, offers a principled way to uncover latent structure, since observations with similar importance patterns may share predictive dynamics. In a prior work, Feature Oriented Representation with Clustering and Explanation (FORCE) we showed that this approach measurably improved neural network performance. In this article, we present JEDI-net(Joint Embedding with Dynamic Integration Network) a unified architecture that periodically clusters SHAP values during optimization to identify latent subgroups with similar feature importance patterns, embed their identities directly into the network, allowing predictions to adapt to each observations predictive structure.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.17314 [cs.LG]
(or arXiv:2610.17314v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.17314 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 268 words
