7 citations · 10 across the 10 of their papers we have counts for
10 papers
Uncertainty-Aware Post-Hoc Calibration: Mitigating Confidently Incorrect Predictions Beyond Calibration Metrics
Hassan Gharoun, Mohammad Sadegh Khorshidi, Kasra Ranjbarigderi +2
Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneou…
Domain-Informed Genetic Superposition Programming: A Case Study on SFRC Beams
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun +3
This study presents domain-informed genetic superposition programming (DIGSP), a symbolic regression framework tailored for engineering systems governed by separable physical mecha…
From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun +3
We study symbolic surrogate modeling of frozen Transformer embeddings to obtain compact, auditable classifiers with calibrated probabilities. For five benchmarks (SST2G, 20NG, MNIS…
Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun +3
This paper introduces Multi-population Ensemble Genetic Programming (MEGP), a computational intelligence framework that integrates cooperative coevolution and the multiview learnin…
Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks
Hassan Gharoun, Mohammad Sadegh Khorshidi, Kasra Ranjbarigderi +2
This work proposes an evidence-retrieval mechanism for uncertainty-aware decision-making that replaces a single global cutoff with an evidence-conditioned, instance-adaptive criter…
Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification
Hassan Gharoun, Mohammad Sadegh Khorshidi, Fang Chen +1
Reliable uncertainty quantification is critical in high-stakes applications, such as medical diagnosis, where confidently incorrect predictions can erode trust in automated decisio…
Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning
Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun +4
In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Add…
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection
Hassan Gharoun, Navid Yazdanjoe, Mohammad Sadegh Khorshidi +1
With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality), the burden of the curse of dimensionality has become increasingly…
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun +3
In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches…
Meta-learning approaches for few-shot learning: A survey of recent advances
Hassan Gharoun, Fereshteh Momenifar, Fang Chen +1
Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution…