1 code implementation • 22 Apr 2024 • Zichuan Liu, Zefan Wang, Linjie Xu, Jinyu Wang, Lei Song, Tianchun Wang, Chunlin Chen, Wei Cheng, Jiang Bian
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
no code implementations • 15 Apr 2024 • Linjie Xu, Zichuan Liu, Alexander Dockhorn, Diego Perez-Liebana, Jinyu Wang, Lei Song, Jiang Bian
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
1 code implementation • 16 Jan 2024 • Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
no code implementations • 25 Oct 2023 • Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Lunting Fan, Lingfei Wu, Qingsong Wen
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
no code implementations • 12 May 2023 • Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu, Zhi Wang, Chunlin Chen
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition.
no code implementations • 15 Sep 2022 • Zichuan Liu, Yuanyang Zhu, Zhi Wang, Yang Gao, Chunlin Chen
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network architecture makes decisions in an opaque manner that hinders humans from understanding the learned knowledge and how input observations influence decisions.
Multi-agent Reinforcement Learning reinforcement-learning +3
1 code implementation • 15 Sep 2021 • Zichuan Liu, Zhaoyang Wu, Meng Wang, Rui Zhang
Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics.
no code implementations • 21 May 2021 • Zichuan Liu, Rui Zhang, Chen Wang, Zhu Xiao, Hongbo Jiang
In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations.
1 code implementation • 7 Jan 2021 • Sheng Yang, Weisi Lin, Guosheng Lin, Qiuping Jiang, Zichuan Liu
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images.
no code implementations • CVPR 2019 • Zichuan Liu, Guosheng Lin, Sheng Yang, Fayao Liu, Weisi Lin, Wang Ling Goh
It is challenging to detect curve texts due to their irregular shapes and varying sizes.
no code implementations • 30 Sep 2018 • Zichuan Liu, Guosheng Lin, Wang Ling Goh, Fayao Liu, Chunhua Shen, Xiaokang Yang
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN).
no code implementations • CVPR 2018 • Zichuan Liu, Guosheng Lin, Sheng Yang, Jiashi Feng, Weisi Lin, Wang Ling Goh
MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph.
no code implementations • 20 Feb 2017 • Yixing Li, Zichuan Liu, Kai Xu, Hao Yu, Fengbo Ren
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
no code implementations • 12 Dec 2016 • Zichuan Liu, Yixing Li, Fengbo Ren, Hao Yu
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP).