most citedAdaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Dissecting Fine-Tuning Unlearning in Large Language Models

Yihuai Hong, Yuelin Zou, Lijie Hu +3

Fine-tuning-based unlearning methods prevail for preventing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabiliti…

cs.CL20241 cited

Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines

Kangtong Mo, Wenyan Liu, Xuanzhen Xu +3

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and La…

cs.IR2024

Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising

Chang Zhou, Yang Zhao, Yuelin Zou +4

This paper proposes new methods to enhance click-through rate (CTR) prediction models using the Deep Interest Network (DIN) model, specifically applied to the advertising system of…

cs.RO2024

TD3 Based Collision Free Motion Planning for Robot Navigation

Hao Liu, Yi Shen, Chang Zhou +3

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL…

cs.RO20241 cited

Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning

Hao Liu, Yi Shen, Wenjing Zhou +3

In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep…