methodology-3

Data Interaction

3260 papers • 126 benchmarks • 313 datasets

Measure and value the train data interaction to interpret their contribution to the model's performance.

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Most implemented papers

A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

Sunwoo Ha, S. Monadjemi, R. Garnett, Alvitta Ottley•Mon Aug 08 2022

This paper compares and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets and analyzes exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures.

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Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

H. Sari, Yuxin Ji, Yu Wang, Haitao Zhao, Guan Gui, H. Gačanin, Fumiyuki Adachi•Sat Sep 30 2023

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

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Meta Prompting for AI Systems

Yifan Zhang, Yang Yuan, A. C. Yao•Sun Nov 19 2023

Recursive Meta Prompting is introduced, a framework that elevates the reasoning capabilities of large language models (LLMs) by focusing on the formal structure of a task rather than content-specific examples, and formalized as a functor that maps a category of tasks to a category of structured prompts.

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