Zobrazeno 1 - 10
of 19 682
pro vyhledávání: '"Zhang, Ji"'
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works mainly focus on describing the superficial associative relations among
Externí odkaz:
http://arxiv.org/abs/2406.09095
Autor:
Bai, Jun, Wu, Di, Shelley, Tristan, Schubel, Peter, Twine, David, Russell, John, Zeng, Xuesen, Zhang, Ji
Material defects (MD) represent a primary challenge affecting product performance and giving rise to safety issues in related products. The rapid and accurate identification and localization of MD constitute crucial research endeavours in addressing
Externí odkaz:
http://arxiv.org/abs/2406.07880
Autor:
Wang, Junyang, Xu, Haiyang, Jia, Haitao, Zhang, Xi, Yan, Ming, Shen, Weizhou, Zhang, Ji, Huang, Fei, Sang, Jitao
Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their training data, lack the capability to function effectively as operation ass
Externí odkaz:
http://arxiv.org/abs/2406.01014
The paper investigates the distributed estimation problem under low bit rate communications. Based on the signal-comparison (SC) consensus protocol under binary-valued communications, a new consensus+innovations type distributed estimation algorithm
Externí odkaz:
http://arxiv.org/abs/2405.18694
Data constitute the foundational component of the data economy and its marketplaces. Efficient and fair data valuation has emerged as a topic of significant interest.\ Many approaches based on marginal contribution have shown promising results in var
Externí odkaz:
http://arxiv.org/abs/2404.19557
Charts are important for presenting and explaining complex data relationships. Recently, multimodal large language models (MLLMs) have shown remarkable capabilities in various chart understanding tasks. However, the sheer size of these models in term
Externí odkaz:
http://arxiv.org/abs/2404.16635
This paper introduces a real-time algorithm for navigating complex unknown environments cluttered with movable obstacles. Our algorithm achieves fast, adaptable routing by actively attempting to manipulate obstacles during path planning and adjusting
Externí odkaz:
http://arxiv.org/abs/2404.07447
This paper proposes a new distributed nonconvex stochastic optimization algorithm that can achieve privacy protection, communication efficiency and convergence simultaneously. Specifically, each node adds time-varying privacy noises to its local stat
Externí odkaz:
http://arxiv.org/abs/2403.18254
Language agents have demonstrated autonomous decision-making abilities by reasoning with foundation models. Recently, efforts have been made to train language agents for performance improvement, with multi-step reasoning and action trajectories as th
Externí odkaz:
http://arxiv.org/abs/2403.14589
Autor:
Chen, Hongzhan, Chen, Hehong, Yan, Ming, Xu, Wenshen, Gao, Xing, Shen, Weizhou, Quan, Xiaojun, Li, Chenliang, Zhang, Ji, Huang, Fei, Zhou, Jingren
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human behaviors. While prior research has predominantly focused on enhancin
Externí odkaz:
http://arxiv.org/abs/2403.13679