1 code implementation • 16 Feb 2024 • Yinpeng Liu, Jiawei Liu, Xiang Shi, Qikai Cheng, Wei Lu
We advocate the few-shot in-context curriculum learning (ICCL), a simple but effective demonstration ordering method for ICL, which implies gradually increasing the complexity of prompt demonstrations during the inference process.
no code implementations • 19 Oct 2023 • Xiang Shi, Jiawei Liu, Yinpeng Liu, Qikai Cheng, Wei Lu
The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches.
no code implementations • 9 Aug 2023 • Guillermo Bernárdez, José Suárez-Varela, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
The ECN configuration is thus a crucial aspect on the performance of CC protocols.
no code implementations • 31 Mar 2023 • Guillermo Bernárdez, José Suárez-Varela, Albert López, Xiang Shi, Shihan Xiao, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
In this paper, we present MAGNNETO, a distributed ML-based framework that leverages Multi-Agent Reinforcement Learning and Graph Neural Networks for distributed TE optimization.
2 code implementations • 22 Dec 2022 • Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela, Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros, Albert Cabellos-Aparicio
We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e. g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations.
1 code implementation • 1 Feb 2022 • Carlos Güemes-Palau, Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio
In the context of DTN, DRL can be leveraged to solve optimization problems without directly impacting the real-world network behavior.
1 code implementation • 29 Dec 2021 • José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés, Krzysztof Rusek, Fabien Geyer, Xiangle Cheng, Xiang Shi, Shihan Xiao, Franco Scarselli, Albert Cabellos-Aparicio, Pere Barlet-Ros
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e. g., chemistry, biology, recommendation systems).
1 code implementation • 22 Sep 2021 • Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros, Albert Cabellos-Aparicio
In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process.
no code implementations • 7 Mar 2021 • Binyuan Hui, Xiang Shi, Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu
In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.