arXiv Machine Learning July 9, 2026 By Yifan Zhang, Yuxin Hu, Zhuobin Hao, Xiaozhuan Gao, Lipeng Pan

UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2607. 06638v1 Announce Type: new Abstract: Self-paced learning (SPL) is an effective learning paradigm that simulates the human learning process by progressing from easy to difficult samples based on the value of the loss function during the learning process.

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

safety

arXiv AI

Jul 29

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607. 25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evidence lower bound (ELBO) serving as the standard objective function.

By Pei-Hsuan Hsia, Lars H. Heyen, Arvid Weyrauch, Markus Goetz, Achim Streit, Sebastian Krumscheid, Charlotte Debus

arXiv Machine Learning

Jul 16

Variational Inference for Evidential Deep Learning

arXiv:2605. 26477v2 Announce Type: replace Abstract: While Deep Neural Networks (DNNs) achieve remarkable performance, their tendency to produce overconfident predictions.

By Jiawei Tang, Xinyan Du, Hui Liu, Junhui Hou, Yuheng Jia

agents robotics 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

Jun 19

ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification

arXiv:2606. 19371v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a fatal disorder that destroys memory and cognitive skills in the elderly population.

By Long Doan, Branden Chen, Ethan Litton, Huan Huang, Jiajing Huang, Yixin Xie, Weihua Zhou, Nandakumar Narayanan, Chen Zhao

multimodal

arXiv AI

Jul 13

PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training

arXiv:2603. 05361v2 Announce Type: replace Abstract: 9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances.

By Zirong Chen, Hongchao Zhang, Meiyi Ma

benchmarks safety

arXiv Machine Learning

Jun 24

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning

arXiv:2602. 01477v2 Announce Type: replace-cross Abstract: Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.

By Pietro Carlotti, Nevena Gligi\'c, Arya Farahi

safety

Related stories

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

Variational Inference for Evidential Deep Learning

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

ProMUSE: Progressive Multi-modal Uncertainty-guided Staged Evidential Alzheimer Disease Classification

PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training

Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning