Author

I. Aguilar

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Open access Oct 2026

Safety-Relevant Biomedical Machine Learning Should Adopt Stability-First Reporting

Biomedical Machine Learning (ML) is increasingly evaluated through Independent and Identically Distributed (IID) benchmarks, even when deployment involves irregular observation, missingness, distribution shift, and high failure costs. This position paper argues for a stability-oriented reporting standard for safety-rel...

Z. A. Zainal, O. Kavehei, I. Aguilar et al. · medRxiv · 0 citations

#artificial intelligence Preprint Sep 2026

Normative Loss Landscape Navigation: A Trajectory-Based Approach to Mitigating Forgetting in Incremental Learning

TMLN (Trajectory-Modulatory Landscape Navigation), a normative navigation policy that formalizes continual learning as an optimal control problem over a curved loss landscape, is proposed and significantly reduces the loss barrier between consecutive tasks.

I. Aguilar, Z. A. Zainal, Luis Fernando Herbozo Contreras et al. · 0 citations

#machine learning Mar 2025

Stochastic Engrams for Efficient Continual Learning with Binarized Neural Networks

A novel approach is proposed that integrates stochastically-activated engrams as a gating mechanism for metaplastic binarized neural networks (mBNNs) that leverages the computational efficiency of mBNNs combined with the robustness of probabilistic memory traces to mitigate forgetting and maintain the model's reliabili...

I. Aguilar, Luis Fernando Herbozo Contreras, O. Kavehei · arXiv.org · 2 citations · ⚡1

Preprint Jul 2026

Metaplasticity as adaptive gradient preconditioning for incremental learning

By mathematically formalizing continuous biological metaplasticity as stable gradient-based regularization, SynGAP offers a highly robust and memory-efficient solution for adaptive intelligence at the edge.

I. Aguilar, Z. A. Zainal, O. Kavehei · 0 citations