1 citations · 2 across the 19 of their papers we have counts for
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Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models
Sandra Leticia Juárez-Osorio, Jorge I. Hernandez-Martinez, Jesus Ivan Ruiz-Martinez +2
We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline. Using stationary harmonic-mix…
LTBs-KAN: Linear-Time B-splines Kolmogorov-Arnold Networks
Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Kolmogorov-Arnold Networks (KANs) are a recent neural network architecture offering an alternative to Multilayer Perceptrons (MLPs) with improved explainability and expressibility.…
Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine
Jorge I. Hernandez-Martinez, Andres Mendez-Vazquez, Gerardo Rodriguez-Hernandez +1
We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schrödinger equation in configuration space. Traditional Configuration Interaction (CI) me…
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Ivan Reyes-Amezcua, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz +2
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial…
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction
Mauricio Mendez-Ruiz, Ivan Garcia Jorge Gonzalez-Zapata, Gilberto Ochoa-Ruiz +1
Few-shot learning is a relatively new technique that specializes in problems where we have little amounts of data. The goal of these methods is to classify categories that have not…