1 code implementation • 16 May 2024 • Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C. M. Leung
With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields.
no code implementations • 22 Jan 2024 • Kristina Dzeparoska, Jieyu Lin, Ali Tizghadam, Alberto Leon-Garcia
And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand.
no code implementations • 9 Jan 2022 • Sai Qian Zhang, Jieyu Lin, Qi Zhang
Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model by aggregating locally-computed models without exposing their raw data.
no code implementations • ACL 2021 • Jieyu Lin, Jiajie Zou, Nai Ding
We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options.
1 code implementation • NeurIPS 2020 • Sai Qian Zhang, Jieyu Lin, Qi Zhang
Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 8 Mar 2020 • Jieyu Lin, Kristina Dzeparoska, Sai Qian Zhang, Alberto Leon-Garcia, Nicolas Papernot
Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations.
Multi-agent Reinforcement Learning reinforcement-learning +1
2 code implementations • NeurIPS 2019 • Sai Qian Zhang, Qi Zhang, Jieyu Lin
Multi-agent reinforcement learning (MARL) has recently received considerable attention due to its applicability to a wide range of real-world applications.
Multi-agent Reinforcement Learning reinforcement-learning +3