3 citations · 6 across the 4 of their papers we have counts for
4 papers
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
Hasib-Al Rashid, Argho Sarkar, Aryya Gangopadhyay +2
Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices. Tiny Machine Learning (tinyML) has emerged a…
LLM Augmented Hierarchical Agents
Bharat Prakash, Tim Oates, Tinoosh Mohsenin
Solving long-horizon, temporally-extended tasks using Reinforcement Learning (RL) is challenging, compounded by the common practice of learning without prior knowledge (or tabula r…
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents
Tejaswini Manjunath, Mozhgan Navardi, Prakhar Dixit +2
Robots have been successfully used to perform tasks with high precision. In real-world environments with sparse rewards and multiple goals, learning is still a major challenge and…
TinyMNet: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
Hasib-Al Rashid, Pretom Roy Ovi, Carl Busart +2
With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of…