Zobrazeno 1 - 10
of 1 038
pro vyhledávání: '"Zhou Dawei"'
Autor:
Beigi, Mohammad, Wang, Sijia, Shen, Ying, Lin, Zihao, Kulkarni, Adithya, He, Jianfeng, Chen, Feng, Jin, Ming, Cho, Jin-Hee, Zhou, Dawei, Lu, Chang-Tien, Huang, Lifu
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current metho
Externí odkaz:
http://arxiv.org/abs/2410.20199
Continual Learning (CL) aims to learn in non-stationary scenarios, progressively acquiring and maintaining knowledge from sequential tasks. Recent Prompt-based Continual Learning (PCL) has achieved remarkable performance with Pre-Trained Models (PTMs
Externí odkaz:
http://arxiv.org/abs/2409.18860
Many real-world graphs frequently present challenges for graph learning due to the presence of both heterophily and heterogeneity. However, existing benchmarks for graph learning often focus on heterogeneous graphs with homophily or homogeneous graph
Externí odkaz:
http://arxiv.org/abs/2407.10916
Autor:
Hoggenmuller, Marius, Tomitsch, Martin, Parker, Callum, Nguyen, Trung Thanh, Zhou, Dawei, Worrall, Stewart, Nebot, Eduardo
The advent of cyber-physical systems, such as robots and autonomous vehicles (AVs), brings new opportunities and challenges for the domain of interaction design. Though there is consensus about the value of human-centred development, there is a lack
Externí odkaz:
http://arxiv.org/abs/2406.08733
Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during training. Existing methods insufficiently address the removal of size informa
Externí odkaz:
http://arxiv.org/abs/2406.04601
Adversarial training (AT) trains models using adversarial examples (AEs), which are natural images modified with specific perturbations to mislead the model. These perturbations are constrained by a predefined perturbation budget $\epsilon$ and are e
Externí odkaz:
http://arxiv.org/abs/2406.00685
Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechanical damages and Internet outages. Imputing the missing values is cruci
Externí odkaz:
http://arxiv.org/abs/2403.11960
Inspired by recent experimental synthesis of the two-dimensional Janus material MoSH, we have performed extensive first-principles calculations to investigate the characteristics of all possible Janus two-dimensional transition metal hydrosulfides (J
Externí odkaz:
http://arxiv.org/abs/2401.15577
Deep neural networks are vulnerable to adversarial samples. Adversarial fine-tuning methods aim to enhance adversarial robustness through fine-tuning the naturally pre-trained model in an adversarial training manner. However, we identify that some la
Externí odkaz:
http://arxiv.org/abs/2401.14707
Deep neural networks are vulnerable to adversarial noise. Adversarial Training (AT) has been demonstrated to be the most effective defense strategy to protect neural networks from being fooled. However, we find AT omits to learning robust features, r
Externí odkaz:
http://arxiv.org/abs/2310.03358