5,248,195 AI resources indexedWorking toward the world’s largest AI resource index.
Publications about AI methods and applications.
Research paper by Aleakhya Pandeti and collaborators, published 2026 in arXiv (Cornell University). Research area: AI in cancer detection.
Research paper by Yinsong Chen and collaborators. arXiv record dated 2026-05-21. Research area: Machine learning.
Research paper by Dorsa Macky Aleagha and collaborators. arXiv record dated 2026-04-30. Research area: Artificial intelligence.
Research paper by Thomas Jantos and collaborators. arXiv record dated 2025-09-01. Research area: Computer vision.
Research paper by Phi Van Nguyen and collaborators. arXiv record dated 2026-04-07. Research area: Computer vision.
Research paper by Falaah Arif Khan and collaborators. arXiv record dated 2025-07-12. Research area: Machine learning.
Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to learn a probabilistic representation using Monte Carlo dropout. Our results outperform the state-of-the-art of deterministic networks. We attribute this improvement to a better probabilistic feature space representation on the encoder and the Bayesian variabil
Research paper by Simon Baur and collaborators. arXiv record dated 2026-08-14. Research area: Computer vision.
Research paper by Maciej Skorski and collaborators. Published 2025-08-19. Topics: Bayesian evaluation, aleatoric uncertainty, epistemic uncertainty, model domain sensitivity, GPU-optimized Bayesian framework.
Research paper by Revista, Zen and collaborators, published 2025 in Zenodo (CERN European Organization for Nuclear Research). Research area: Adversarial Robustness in Machine Learning.
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's mot
Research paper by Masen Bachleda and collaborators. arXiv record dated 2026-07-30. Research area: Machine learning.
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