Networking Systems for Video Anomaly Detection: A Tutorial and Survey

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.

LLM-based policy generation for intent-based management of applications

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.

Management

A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning

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.

Federated Learning Multi-agent Reinforcement Learning +2

Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models

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.

Machine Reading Comprehension

Succinct and Robust Multi-Agent Communication With Temporal Message Control

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)

On the Robustness of Cooperative Multi-Agent Reinforcement Learning

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

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

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