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Akademický článek

Analysis of Film and Television + Professional Cluster Resource Integration Model and Industry Development Dynamics

Autor: Zhang Huixin, Liu Zexi

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

This paper proposes a resource integration model for film and television + professional clusters and builds a corresponding digital platform to realize the effective integration of film and television professional resources. Using XML technology of d

Externí odkaz: https://doaj.org/article/78a9da9bcd0d4c819a0f7de23599d7b7

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Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation

Autor: Tang, Shuo, Pang, Xianghe, Liu, Zexi, Tang, Bohan, Ye, Rui, Dong, Xiaowen, Wang, Yanfeng, Chen, Siheng

Post-training is essential for enabling large language models (LLMs) to follow human instructions. Inspired by the recent success of using LLMs to simulate human society, we leverage multi-agent simulation to automatically generate diverse text-based

Externí odkaz: http://arxiv.org/abs/2410.14251

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Training-Free Message Passing for Learning on Hypergraphs

Autor: Tang, Bohan, Liu, Zexi, Jiang, Keyue, Chen, Siheng, Dong, Xiaowen

Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to generate informative node features for various downstream tasks like node

Externí odkaz: http://arxiv.org/abs/2402.05569

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Hypergraph Transformer for Semi-Supervised Classification

Autor: Liu, Zexi, Tang, Bohan, Ye, Ziyuan, Dong, Xiaowen, Chen, Siheng, Wang, Yanfeng

Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performa

Externí odkaz: http://arxiv.org/abs/2312.11385

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