arXiv AI August 12, 2026 By M. Sajid, A. Quadir, A. Rahaman, P. N. Suganthan, M. Tanveer

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

arXiv:2608. 10007v1 Announce Type: cross Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied to real-world datasets containing noise and outliers.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv Machine Learning

22h ago

RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network

arXiv:2608. 16965v1 Announce Type: new Abstract: The dominance of majority classes in real-world datasets poses a fundamental challenge to randomized neural networks, often biasing decision boundaries and overlooking critical minority samples.

By A. Rahaman, A. Quadir, M. Tanveer

benchmarks

arXiv AI

Aug 7

Evidential Rule Learning for Interpretable Classification with Abstention

arXiv:2608. 05859v1 Announce Type: cross Abstract: Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably.

By Javier Fumanal-Idocin, Javier Andreu-Perez

benchmarks

arXiv Machine Learning

Aug 11

ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning

arXiv:2608. 09768v1 Announce Type: new Abstract: 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.

By Ange-Cl\'ement Akazan, Ineza Remy Mugenga, Abebe Geletu, Jean Medard Ngnotchouye, Issa Karambal

benchmarks

arXiv AI

1d ago

ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

arXiv:2608. 14773v1 Announce Type: cross Abstract: The efficient-KAN literature---covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network---has been benchmarked almost entirely on clean data.

By Harshil Lodhiya

arXiv Machine Learning

Aug 7

Worst-Case Distance-Aware Error Bounds for Neural Networks

arXiv:2510. 22021v3 Announce Type: replace Abstract: Safety-critical applications of machine learning require uncertainty estimates that support reliable worst-case analysis.

By Masoud Ataei, Vikas Dhiman, Mohammad Javad Khojasteh

agents safety

arXiv Machine Learning

Jun 2

Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness

arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.

By Kai Wang

fine-tuning benchmarks safety

Related stories

RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network

Evidential Rule Learning for Interpretable Classification with Abstention

ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning

ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

Worst-Case Distance-Aware Error Bounds for Neural Networks

Sensitivity as a Double-Edged Sword: A Trade-off Between Discriminability and Adversarial Robustness