Výsledky vyhledávání

Report

Dissecting Fine-Tuning Unlearning in Large Language Models

Autor: Hong, Yihuai, Zou, Yuelin, Hu, Lijie, Zeng, Ziqian, Wang, Di, Yang, Haiqin

Fine-tuning-based unlearning methods prevail for preventing targeted harmful, sensitive, or copyrighted information within large language models while preserving overall capabilities. However, the true effectiveness of these methods is unclear. In th

Externí odkaz: http://arxiv.org/abs/2410.06606

Zobrazit plný text záznamu

Report

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

Autor: Mo, Kangtong, Liu, Wenyan, Xu, Xuanzhen, Yu, Chang, Zou, Yuelin, Xia, Fangqing

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 Large Language Models (LLM), we analyze sentiment from the perspective o

Externí odkaz: http://arxiv.org/abs/2406.13626

Zobrazit plný text záznamu

Report

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

Autor: Zhou, Chang, Zhao, Yang, Zou, Yuelin, Cao, Jin, Fan, Wenhan, Zhao, Yi, Cheng, Chiyu

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 Alibaba's Taobao platform. Unlike traditional deep learning approache

Externí odkaz: http://arxiv.org/abs/2406.10239

Zobrazit plný text záznamu

Report

TD3 Based Collision Free Motion Planning for Robot Navigation

Autor: Liu, Hao, Shen, Yi, Zhou, Chang, Zou, Yuelin, Gao, Zijun, Wang, Qi

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm

Externí odkaz: http://arxiv.org/abs/2405.15460

Zobrazit plný text záznamu

Report

Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning

Autor: Liu, Hao, Shen, Yi, Zhou, Wenjing, Zou, Yuelin, Zhou, Chang, He, Shuyao

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 Q-Network (DDQN), to develop a local navigation system that adapts to

Externí odkaz: http://arxiv.org/abs/2404.17379

Zobrazit plný text záznamu