Zobrazeno 1 - 10
of 6 394
pro vyhledávání: '"Guo, Xu"'
Natural Language Counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactuals provide insight into the reasoning behind a model's predictions by h
Externí odkaz:
http://arxiv.org/abs/2407.03993
Autor:
Wang, Shang, Zhu, Tianqing, Liu, Bo, Ding, Ming, Guo, Xu, Ye, Dayong, Zhou, Wanlei, Yu, Philip S.
With the rapid development of artificial intelligence, large language models (LLMs) have made remarkable advancements in natural language processing. These models are trained on vast datasets to exhibit powerful language understanding and generation
Externí odkaz:
http://arxiv.org/abs/2406.07973
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes. Training with CAD enhances model robustness against spurious featur
Externí odkaz:
http://arxiv.org/abs/2406.06633
High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this c
Externí odkaz:
http://arxiv.org/abs/2405.14652
In this paper, we construct and analyze new first- and second-order implicit-explicit (IMEX) schemes for the unsteady Navier-Stokes-Darcy model to describe the coupled free flow-porous media system, which is based on the scalar auxiliary variable (SA
Externí odkaz:
http://arxiv.org/abs/2405.11223
Autor:
Wu, Qing, Guo, Xu, Chen, Lixuan, He, Dongming, Wei, Hongjiang, Wang, Xudong, Zhou, S. Kevin, Zhang, Yifeng, Yu, Jingyi, Zhang, Yuyao
Emerging unsupervised reconstruction techniques based on implicit neural representation (INR), such as NeRP, CoIL, and SCOPE, have shown unique capabilities in CT linear inverse imaging. In this work, we propose a novel unsupervised density neural re
Externí odkaz:
http://arxiv.org/abs/2405.07047
This paper is concerned with estimation and inference for ultrahigh dimensional partially linear single-index models. The presence of high dimensional nuisance parameter and nuisance unknown function makes the estimation and inference problem very ch
Externí odkaz:
http://arxiv.org/abs/2404.04471
The surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability, especially in high-stakes applications, such as healthcare and autonomous driving. Due to the lack of a rigorous definition of exp
Externí odkaz:
http://arxiv.org/abs/2403.10415
Autor:
Guo, Xu, Chen, Yiqiang
The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform c
Externí odkaz:
http://arxiv.org/abs/2403.04190
Autor:
Pan, Fang, Zhai, Junni, Chen, Jinyu, Yang, Lin, Dong, Hua, Yuan, Fang, Jiang, Zhuangde, Ren, Wei, Ye, Zuo-Guang, Zhang, Guo-Xu, Li, Jingrui
Stability is one of the key issues in mixed-halide perovskite alloys which are promising in emergent optoelectronics. Previous density-functional-theory (DFT) and machine learning studies indicate that the formation-energy convex hulls of these mater
Externí odkaz:
http://arxiv.org/abs/2402.19274