2 citations · 2 across the 2 of their papers we have counts for
5 papers
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Sheila Schoepp, Mehran Taghian, Shotaro Miwa +3
Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplica…
WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation
Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka +2
Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retr…
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
Yoshihiro Mitsuka, Shadan Golestan, Zahin Sufiyan +2
Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially regarding how past training tasks…
The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning
Sheila Schoepp, Masoud Jafaripour, Yingyue Cao +6
Reinforcement learning (RL) has shown impressive results in sequential decision-making tasks. Meanwhile, Large Language Models (LLMs) and Vision-Language Models (VLMs) have emerged…
A Study of the Efficacy of Generative Flow Networks for Robotics and Machine Fault-Adaptation
Zahin Sufiyan, Shadan Golestan, Shotaro Miwa +2
Advancements in robotics have opened possibilities to automate tasks in various fields such as manufacturing, emergency response and healthcare. However, a significant challenge th…