Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Zhang, Zecheng"'
Autor:
Xu, Tianqi, Chen, Linyao, Wu, Dai-Jie, Chen, Yanjun, Zhang, Zecheng, Yao, Xiang, Xie, Zhiqiang, Chen, Yongchao, Liu, Shilong, Qian, Bochen, Torr, Philip, Ghanem, Bernard, Li, Guohao
The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM age
Externí odkaz:
http://arxiv.org/abs/2407.01511
Image anomaly detection is a popular research direction, with many methods emerging in recent years due to rapid advancements in computing. The use of artificial intelligence for image anomaly detection has been widely studied. By analyzing images of
Externí odkaz:
http://arxiv.org/abs/2406.13987
This research delves deeply into Meta Reinforcement Learning (Meta RL) through a exploration focusing on defining generalization limits and ensuring convergence. By employing a approach this article introduces an innovative theoretical framework to m
Externí odkaz:
http://arxiv.org/abs/2405.13290
Autor:
Peng, Xirui, Xu, Qiming, Feng, Zheng, Zhao, Haopeng, Tan, Lianghao, Zhou, Yan, Zhang, Zecheng, Gong, Chenwei, Zheng, Yingqiao
This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rap
Externí odkaz:
http://arxiv.org/abs/2405.10492
Foundation models, such as large language models, have demonstrated success in addressing various language and image processing tasks. In this work, we introduce a multi-modal foundation model for scientific problems, named PROSE-PDE. Our model, desi
Externí odkaz:
http://arxiv.org/abs/2404.12355
Autor:
Zhang, Zecheng
The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL). However, recent advances have rapidly expanded this to include the approximation
Externí odkaz:
http://arxiv.org/abs/2404.02892
Autor:
Hu, Weihua, Yuan, Yiwen, Zhang, Zecheng, Nitta, Akihiro, Cao, Kaidi, Kocijan, Vid, Leskovec, Jure, Fey, Matthias
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstra
Externí odkaz:
http://arxiv.org/abs/2404.00776
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression. Initially, we enhance the
Externí odkaz:
http://arxiv.org/abs/2402.15406
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit
Externí odkaz:
http://arxiv.org/abs/2310.18888
This study focuses on addressing the inverse source problem associated with the parabolic equation. We rely on sparse boundary flux data as our measurements, which are acquired from a restricted section of the boundary. While it has been established
Externí odkaz:
http://arxiv.org/abs/2310.01541