Search Results for author: Qingyang Zhang

Found 7 papers, 4 papers with code

Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs

1 code implementation22 Jul 2023 Qingyang Zhang, Yiming Yang, Jingqing Ruan, Xuantang Xiong, Dengpeng Xing, Bo Xu

However, existing works often overlook the temporal coherence in GCHRL when learning latent subgoal representations and lack an efficient subgoal selection strategy that balances exploration and exploitation.

Continuous Control Hierarchical Reinforcement Learning +2

Provable Dynamic Fusion for Low-Quality Multimodal Data

1 code implementation3 Jun 2023 Qingyang Zhang, Haitao Wu, Changqing Zhang, QinGhua Hu, Huazhu Fu, Joey Tianyi Zhou, Xi Peng

The inherent challenge of multimodal fusion is to precisely capture the cross-modal correlation and flexibly conduct cross-modal interaction.

Vector Autoregressive Evolution for Dynamic Multi-Objective Optimisation

no code implementations22 May 2023 Shouyong Jiang, Yong Wang, Yaru Hu, Qingyang Zhang, Shengxiang Yang

Dynamic multi-objective optimisation (DMO) handles optimisation problems with multiple (often conflicting) objectives in varying environments.

Evolutionary Algorithms

X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages

1 code implementation7 May 2023 Feilong Chen, Minglun Han, Haozhi Zhao, Qingyang Zhang, Jing Shi, Shuang Xu, Bo Xu

(3) Integrating multiple modalities: all single-modal encoders are aligned with the LLM through X2L interfaces to integrate multimodal capabilities into the LLM.

Instruction Following Language Modelling +3

Given Users Recommendations Based on Reviews on Yelp

1 code implementation3 Dec 2021 Shuwei Zhang, Maiqi Tang, Qingyang Zhang, Yucan Luo, Yuhui Zou

For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert model and word2vec model; the second part is the implementation of an item-based collaborative filtering algorithm to compute the similarity of each review under different categories of restaurants.

Collaborative Filtering Recommendation Systems

Computing Systems for Autonomous Driving: State-of-the-Art and Challenges

no code implementations30 Sep 2020 Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu, Yongtao Yao, Qingyang Zhang, Weisong Shi

The recent proliferation of computing technologies, e. g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e. g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.

Distributed, Parallel, and Cluster Computing Robotics

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