Zobrazeno 1 - 10
of 1 600
pro vyhledávání: '"Ahmed Nisar"'
Autor:
Aslam, Muhammad Azeem, Jun, Wang, Ahmed, Nisar, Zaman, Muhammad Imran, Yanan, Li, Hongfei, Hu, Shiyu, Wang, Liu, Xin
In multi-label emotion classification, particularly for low-resource languages like Arabic, the challenges of class imbalance and label correlation hinder model performance, especially in accurately predicting minority emotions. To address these issu
Externí odkaz:
http://arxiv.org/abs/2410.03979
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also kno
Externí odkaz:
http://arxiv.org/abs/2409.16392
Autonomous selection of optimal options for data collection from multiple alternatives is challenging in uncertain environments. When secondary information about options is accessible, such problems can be framed as contextual multi-armed bandits (CM
Externí odkaz:
http://arxiv.org/abs/2408.04119
Autor:
Israelsen, Brett, Ahmed, Nisar R., Aitken, Matthew, Frew, Eric W., Lawrence, Dale A., Argrow, Brian M.
How can intelligent machines assess their competencies in completing tasks? This question has come into focus for autonomous systems that algorithmically reason and make decisions under uncertainty. It is argued here that machine self-confidence - a
Externí odkaz:
http://arxiv.org/abs/2407.19631
When a robot autonomously performs a complex task, it frequently must balance competing objectives while maintaining safety. This becomes more difficult in uncertain environments with stochastic outcomes. Enhancing transparency in the robot's behavio
Externí odkaz:
http://arxiv.org/abs/2406.11984
Autor:
Acharya, Aastha, Lee, Caleb, D'Alonzo, Marissa, Shamwell, Jared, Ahmed, Nisar R., Russell, Rebecca
Deep learning offers promising new ways to accurately model aleatoric uncertainty in robotic estimation systems, particularly when the uncertainty distributions do not conform to traditional assumptions of being fixed and Gaussian. In this study, we
Externí odkaz:
http://arxiv.org/abs/2405.20513
Heterogeneous Bayesian decentralized data fusion captures the set of problems in which two robots must combine two probability density functions over non-equal, but overlapping sets of random variables. In the context of multi-robot dynamic systems,
Externí odkaz:
http://arxiv.org/abs/2401.16301
We consider the problem of evaluating dynamic consistency in discrete time probabilistic filters that approximate stochastic system state densities with Gaussian mixtures. Dynamic consistency means that the estimated probability distributions correct
Externí odkaz:
http://arxiv.org/abs/2312.17420
Autor:
Wakayama, Shohei, Ahmed, Nisar
For robotic decision-making under uncertainty, the balance between exploitation and exploration of available options must be carefully taken into account. In this study, we introduce a new variant of contextual multi-armed bandits called observation-
Externí odkaz:
http://arxiv.org/abs/2312.12583
This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments. The inherent opacity of machine learning models, from the perspective of the user,
Externí odkaz:
http://arxiv.org/abs/2312.09033