5 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…
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…
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…