11 citations · 11 across the 10 of their papers we have counts for
10 papers
Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels
Jose Marie Antonio Miñoza, Rex Gregor Laylo, Sebastian C. Ibañez
Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forecasts without uncertainty estim…
eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts
Paulo Mario P. Medina, Jose Marie Antonio Miñoza, Sebastian C. Ibañez
Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outperform baseline Empirical Risk Minimiz…
The Hamilton-Jacobi Theory of Deep Learning
Jose Marie Antonio Miñoza, Erika Fille T. Legara, Christopher P. Monterola
In this paper, training a neural network is identified, exactly, as a search through Hamilton--Jacobi initial-value problems: each gradient step selects the initial data of a visco…
Linearized Attention Cannot Enter the Kernel Regime at Any Practical Width
Jose Marie Antonio Miñoza, Paulo Mario P. Medina, Sebastian C. Ibañez
Understanding whether attention mechanisms converge to the kernel regime is foundational to the validity of influence functions for transformer accountability. Exact NTK characteri…
UltraLIF: Fully Differentiable Spiking Neural Networks via Ultradiscretization and Max-Plus Algebra
Jose Marie Antonio Miñoza
Spiking Neural Networks (SNNs) offer energy-efficient, biologically plausible computation but suffer from non-differentiable spike generation, necessitating reliance on heuristic s…
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
Jose Marie Antonio Miñoza
Physics-Informed Neural Networks (PINNs) provide a mesh-free approach for solving differential equations by embedding physical constraints into neural network training. However, PI…
An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Text
Paula Joy B. Martinez, Jose Marie Antonio Miñoza, Sebastian C. Ibañez
Emotion recognition from social media is critical for understanding public sentiment, but accessing training data has become prohibitively expensive due to escalating API costs and…
ML-EcoLyzer: Quantifying the Environmental Cost of Machine Learning Inference Across Frameworks and Hardware
Jose Marie Antonio Minoza, Rex Gregor Laylo, Christian F Villarin +1
Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-f…
Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping
Jose Marie Antonio Minoza
Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too coarse for exact numerical simulations. Wh…
COVID-19 Agent-Based Model with Multi-objective Optimization for Vaccine Distribution
Jose Marie Antonio Minoza, Vena Pearl Bongolan, Joshua Frankie Rayo
Now that SARS-CoV-2 (COVID-19) vaccines are developed, it is very important to plan its distribution strategy. In this paper, we formulated a multi-objective linear programming mod…