6 citations · 11 across the 12 of their papers we have counts for
13 papers
A Learning Algorithm for Threshold Boolean Networks with Prescribed Fixed Points
Gonzalo A. Ruz
We present a learning algorithm for inferring threshold Boolean networks (TBNs) with a prescribed set of fixed points. The proposed method employs a custom differentiable loss func…
Classifying Directional Trajectories Near Criticality in the Three-State Majority-Vote Model with Deep Belief Networks and Bidirectional GRUs
Mauricio A. Valle, Gonzalo A. Ruz
In this work, we investigate whether the latent representations learned by a Deep Belief Network (DBN) and a Bidirectional Gated Recurrent Unit (Bi-GRU) can discriminate among four…
Geometric Filtering of LLM-Generated Samples for Few-Shot Text Classification
Benjamín Schindler, Gonzalo A. Ruz
Large language models (LLMs) can generate synthetic training data for text classification, but the quality of generated samples is heterogeneous: some fall in correct class regions…
Exploration of the generative capabilities of Boltzmann machines applied to social systems under the majority rule
Mauricio A. Valle, Gonzalo A. Ruz
We study the generative capabilities of Boltzmann machines to recover systems governed by the majority rule under critical conditions. To this end, we train deep belief networks (D…
Continuous surrogates versus threshold Boolean networks for modeling Arabidopsis ISR gene regulation
Gonzalo A. Ruz
Gene regulatory network modeling often requires balancing predictive accuracy and mechanistic interpretability. In this work, we compare continuous surrogate models and a discrete…
Baseline-Free Policy Optimization for Neural Combinatorial Optimization
Carlos S. Sepúlveda, Gonzalo A. Ruz
Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems. The standard training algorithm, REINFORCE with a rollout baseline, requires maint…
Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios
Carlos S. Sepúlveda, Gonzalo A. Ruz
Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical methods are often compared o…
Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids
Carlos S. Sepúlveda, Gonzalo A. Ruz
Maritime surveillance missions, such as search and rescue and environmental monitoring, rely on the efficient allocation of sensing assets over vast and geometrically complex areas…
ProfileXAI: User-Adaptive Explainable AI
Gilber A. Corrales, Carlos Andrés Ferro Sánchez, Reinel Tabares-Soto +3
ProfileXAI is a model- and domain-agnostic framework that couples post-hoc explainers (SHAP, LIME, Anchor) with retrieval - augmented LLMs to produce explanations for different typ…
h-analysis and data-parallel physics-informed neural networks
Paul Escapil-Inchauspé, Gonzalo A. Ruz
We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics proces…
Physics-informed neural networks for operator equations with stochastic data
Paul Escapil-Inchauspé, Gonzalo A. Ruz
We consider the computation of statistical moments to operator equations with stochastic data. We remark that application of PINNs -- referred to as TPINNs -- allows to solve the i…
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems
Paul Escapil-Inchauspé, Gonzalo A. Ruz
We consider physics-informed neural networks (PINNs) [Raissi et al., J.~Comput. Phys. 278 (2019) 686-707] for forward physical problems. In order to find optimal PINNs configuratio…
Attractor landscapes in Boolean networks with firing memory
Eric Goles, Fabiola Lobos, Gonzalo A. Ruz +1
In this paper we study the dynamical behavior of Boolean networks with firing memory, namely Boolean networks whose vertices are updated synchronously depending on their proper Boo…