Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Lee, JunKyu"'
Autor:
Zhu, Xuqi, Zhang, Huaizhi, Lee, JunKyu, Zhu, Jiacheng, Pal, Chandrajit, Saha, Sangeet, McDonald-Maier, Klaus D., Zhai, Xiaojun
Modern Neural Network (NN) architectures heavily rely on vast numbers of multiply-accumulate arithmetic operations, constituting the predominant computational cost. Therefore, this paper proposes a high-throughput, scalable and energy efficient non-e
Externí odkaz:
http://arxiv.org/abs/2407.02362
Developing domain models is one of the few remaining places that require manual human labor in AI planning. Thus, in order to make planning more accessible, it is desirable to automate the process of domain model generation. To this end, we investiga
Externí odkaz:
http://arxiv.org/abs/2405.06650
The ability to generate multiple plans is central to using planning in real-life applications. Top-quality planners generate sets of such top-cost plans, allowing flexibility in determining equivalent ones. In terms of the order between actions in a
Externí odkaz:
http://arxiv.org/abs/2404.01503
The growing utilization of planning tools in practical scenarios has sparked an interest in generating multiple high-quality plans. Consequently, a range of computational problems under the general umbrella of top-quality planning were introduced ove
Externí odkaz:
http://arxiv.org/abs/2403.03176
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitations that prevent their broader adoption in many real-world systems, w
Externí odkaz:
http://arxiv.org/abs/2402.01602
The advancements in 5G mobile networks and Edge computing offer great potential for services like augmented reality and Cloud gaming, thanks to their low latency and high bandwidth capabilities. However, the practical limitations of achieving optimal
Externí odkaz:
http://arxiv.org/abs/2310.14090
Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have bee
Externí odkaz:
http://arxiv.org/abs/2303.09068
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is
Externí odkaz:
http://arxiv.org/abs/2210.16083
Autor:
Lee, Junkyu, Katz, Michael, Agravante, Don Joven, Liu, Miao, Tasse, Geraud Nangue, Klinger, Tim, Sohrabi, Shirin
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy to integrate with symbolic knowledge, and often efficient, but requires an up-fro
Externí odkaz:
http://arxiv.org/abs/2203.00669
Publikováno v:
In Metabolic Engineering November 2024 86:1-11
