Zobrazeno 1 - 10
of 8 636
pro vyhledávání: '"Bahador, A"'
Everyday decisions often involve many different levels. What connects these higher and lower level decisions hierarchy to one another determines how the cause(s) of failures are interpreted. It is hypothesized that decision confidence guides the assi
Externí odkaz:
http://arxiv.org/abs/2410.23313
Autor:
Beigomi, Bahador, Zhu, Zheng H.
Traditional control methods effectively manage robot operations using models like motion equations but face challenges with issues of contact and friction, leading to unstable and imprecise controllers that often require manual tweaking. Reinforcemen
Externí odkaz:
http://arxiv.org/abs/2409.12273
This paper illustrates the development of two efficient source localization algorithms for electroencephalography (EEG) data, aimed at enhancing real-time brain signal reconstruction while addressing the computational challenges of traditional method
Externí odkaz:
http://arxiv.org/abs/2409.11751
This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation has been ind
Externí odkaz:
http://arxiv.org/abs/2409.01941
Although evidence integration to the boundary model has successfully explained a wide range of behavioral and neural data in decision making under uncertainty, how animals learn and optimize the boundary remains unresolved. Here, we propose a model-f
Externí odkaz:
http://arxiv.org/abs/2408.06080
The Deep operator network (DeepONet) is a powerful yet simple neural operator architecture that utilizes two deep neural networks to learn mappings between infinite-dimensional function spaces. This architecture is highly flexible, allowing the evalu
Externí odkaz:
http://arxiv.org/abs/2407.13010
Autor:
Beigomi, Bahador, Zhu, Zheng H.
Publikováno v:
2024 IEEE International Conference on Robotics and Automation (ICRA)
In this research, we introduce a deep reinforcement learning-based control approach to address the intricate challenge of the robotic pre-grasping phase under microgravity conditions. Leveraging reinforcement learning eliminates the necessity for man
Externí odkaz:
http://arxiv.org/abs/2406.06460
In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known
Externí odkaz:
http://arxiv.org/abs/2405.12456
Autor:
Fuhg, Jan Niklas, Padmanabha, Govinda Anantha, Bouklas, Nikolaos, Bahmani, Bahador, Sun, WaiChing, Vlassis, Nikolaos N., Flaschel, Moritz, Carrara, Pietro, De Lorenzis, Laura
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized tax
Externí odkaz:
http://arxiv.org/abs/2405.03658
How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine ratings f
Externí odkaz:
http://arxiv.org/abs/2403.18868