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Found 6 papers, 2 papers with code
Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning
The ability to generate multiple plans is central to using planning in real-life applications.
Unifying and Certifying Top-Quality Planning
We show that task transformations found in the existing literature can be employed for the efficient certification of various top-quality planning problems and propose a novel transformation to efficiently certify loopless top-quality planning.
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
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.
Hierarchical Reinforcement Learning with AI Planning Models
Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc.
