Exploring Local LLM-based Copilot Capabilities for Enhanced Vehicle Functionalities
This study aims to enhance vehicle functionality by deploying LLM (Large Language Model)-based copilots on local GPUs.
Posts by Collection
portfolio
Richter’s Predictor: Modeling Earthquake Damages
Presenting an algorithmic approach to tackle the Richter’s Predictor: Modeling Earthquake Damage problem, achieving a top 5% rank.
publications
Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach
Published in arXiv, 2023
This paper proposes an Instant Photorealistic Style Transfer (IPST) approach, designed to achieve instant photorealistic style transfer on super-resolution inputs without the need for pre-training on pair-wise datasets or imposing extra constraints.
Recommended citation: Rong Liu, Enyu Zhao, Zhiyuan Liu, Andrew Feng, Scott John Easley. (2023). "Instant Photorealistic Style Transfer: A Lightweight and Adaptive Approach." arXiv preprint arXiv:2309.10011.
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Time-aware MADDPG with LSTM for multi-agent obstacle avoidance: a comparative study
Published in Complex & Intelligent Systems, 2024
This paper addresses the limitations of MADDPG in multi-agent navigation and obstacle avoidance tasks, providing insights for developing intelligent agents and multi-agent systems.
Recommended citation: Enyu Zhao, Ning Zhou, Chanjuan Liu, Houfu Su, Yang Liu & Jinmiao Cong. (2024). "Time-aware MADDPG with LSTM for multi-agent obstacle avoidance: a comparative study." Complex & Intelligent Systems. Volume 10, pages 4141–4155.
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SDI: A tool for speech differentiation in user identification
Published in Expert Systems with Applications, 2024
This paper proposed a speech differentiator with integrated SVM (SDI-SVM) by implementing threshold checking and frequency matching mechanisms.
Recommended citation: Muhammad Abdul Basit, Chanjuan Liu, Enyu Zhao. (2024). "SDI: A tool for speech differentiation in user identification." Expert Systems with Applications. Volume 243, 122866.
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GPT-Fabric: Smoothing and Folding Fabric by Leveraging Pre-Trained Foundation Models
Published in International Symposium of Robotics Research (ISRR), 2024
Propose GPT-Fabric for fabric folding and smoothing, achieve comparable and even better folding and smoothing performance comparing to previous methods with no training data required.
Recommended citation: Vedant Raval*, Enyu Zhao*, Hejia Zhang, Stefanos Nikolaidis, and Daniel Seita. (2024). "GPT-Fabric: Smoothing and Folding Fabric by Leveraging Pre-Trained Foundation Models." International Symposium of Robotics Research (ISRR).
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talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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