7 papers
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
Ange-Clément Akazan, Ineza Remy Mugenga, Abebe Geletu +2
A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken. Empirical ri…
Optimal Stochastic Krylov based Techniques for Large- Scale Log-Determinant Estimation
Verlon Roel Mbingui, Antoine Tambue, Issa Karambal
Estimating the logarithm of the determinant of large sparse positive definite symmetric matrices is an important task in numerical linear algebra, machine learning, Gaussian proces…
Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
Brenda Anague, Bamdad Hosseini, Issa Karambal +1
Recent studies have demonstrated the success of deep learning in solving forward and inverse problems in engineering and scientific computing domains, such as physics-informed neur…
Novel technique based on Léja Points Approximation for Log-determinant Estimation of Large matrices
Verlon Roel Mbingui, Antoine Tambue, Issa Karambal
The computation of the Log-determinant of large, sparse, symmetric positive definite (SPD) matrices is essential in many scientific computational fields such as numerical linear al…
RRaPINNs: Residual Risk-Aware Physics Informed Neural Networks
Ange-Clément Akazan, Issa Karambal, Jean Medard Ngnotchouye +1
Physics-informed neural networks (PINNs) typically minimize average residuals, which can conceal large, localized errors. We propose Residual Risk-Aware Physics-Informed Neural Net…
The Reflexive Integrated Information Unit: A Differentiable Primitive for Artificial Consciousness
Gnankan Landry Regis N'guessan, Issa Karambal
Research on artificial consciousness lacks the equivalent of the perceptron: a small, trainable module that can be copied, benchmarked, and iteratively improved. We introduce the R…