Zobrazeno 1 - 10
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pro vyhledávání: '"An, Qiu"'
Large language models (LLMs) augmented with external data have demonstrated remarkable capabilities in completing real-world tasks. Techniques for integrating external data into LLMs, such as Retrieval-Augmented Generation (RAG) and fine-tuning, are
Externí odkaz:
http://arxiv.org/abs/2409.14924
Emojis have become an integral part of digital communication, enriching text by conveying emotions, tone, and intent. Existing emoji recommendation methods are primarily evaluated based on their ability to match the exact emoji a user chooses in the
Externí odkaz:
http://arxiv.org/abs/2409.10760
In this work, we investigate the shape evolution of rotated, embedded, initially cylindrical grains (with [001] cylinder axis) in Ni under an applied synthetic driving force via molecular dynamics simulations and a continuum, disconnection-based grai
Externí odkaz:
http://arxiv.org/abs/2408.14752
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Autor:
Qiu, Yuqing, Ahmed, Bilal, Abueidda, Diab W., El-Sekelly, Waleed, de Soto, Borja Garcia, Abdoun, Tarek, Ji, Hongli, Qiu, Jinhao, Mobasher, Mostafa E.
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive machine learning based damage identification (CMLDI) method that integrate
Externí odkaz:
http://arxiv.org/abs/2408.03002
Distribution-Level Memory Recall for Continual Learning: Preserving Knowledge and Avoiding Confusion
Autor:
Cheng, Shaoxu, Geng, Kanglei, He, Chiyuan, Qiu, Zihuan, Xu, Linfeng, Qiu, Heqian, Wang, Lanxiao, Wu, Qingbo, Meng, Fanman, Li, Hongliang
Continual Learning (CL) aims to enable Deep Neural Networks (DNNs) to learn new data without forgetting previously learned knowledge. The key to achieving this goal is to avoid confusion at the feature level, i.e., avoiding confusion within old tasks
Externí odkaz:
http://arxiv.org/abs/2408.02695
Autor:
Wu, Kun, Zhu, Yichen, Li, Jinming, Wen, Junjie, Liu, Ning, Xu, Zhiyuan, Qiu, Qinru, Tang, Jian
Learning visuomotor policy for multi-task robotic manipulation has been a long-standing challenge for the robotics community. The difficulty lies in the diversity of action space: typically, a goal can be accomplished in multiple ways, resulting in a
Externí odkaz:
http://arxiv.org/abs/2409.18707
Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as natural ima
Externí odkaz:
http://arxiv.org/abs/2409.18374
This paper presents an experimental configuration to study high-enthalpy radiating flows under nonequilibrium de-excitation. A general design method is introduced, combiningtheoretical analysis and numerical simulations to tailor the flow conditions
Externí odkaz:
http://arxiv.org/abs/2409.17772
This paper introduces a novel approach to enhance time series forecasting using Large Language Models (LLMs) and Generative Agents. With language as a medium, our method adaptively integrates various social events into forecasting models, aligning ne
Externí odkaz:
http://arxiv.org/abs/2409.17515
Reinforcement Learning from Human Feedback aligns the outputs of Large Language Models with human values and preferences. Central to this process is the reward model (RM), which translates human feedback into training signals for optimising LLM behav
Externí odkaz:
http://arxiv.org/abs/2409.17407