Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Hou Zhichao"'
Research on long non-coding RNAs (lncRNAs) has garnered significant attention due to their critical roles in gene regulation and disease mechanisms. However, the complexity and diversity of lncRNA sequences, along with the limited knowledge of their
Externí odkaz:
http://arxiv.org/abs/2411.03522
Implicit models such as Deep Equilibrium Models (DEQs) have emerged as promising alternative approaches for building deep neural networks. Their certified robustness has gained increasing research attention due to security concerns. Existing certifie
Externí odkaz:
http://arxiv.org/abs/2411.00899
Transformer-based architectures have dominated various areas of machine learning in recent years. In this paper, we introduce a novel robust attention mechanism designed to enhance the resilience of transformer-based architectures. Crucially, this te
Externí odkaz:
http://arxiv.org/abs/2410.23182
This work tackles an intriguing and fundamental open challenge in representation learning: Given a well-trained deep learning model, can it be reprogrammed to enhance its robustness against adversarial or noisy input perturbations without altering it
Externí odkaz:
http://arxiv.org/abs/2410.04577
Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains underexplored. Log data, characterized by its hierarchical, dictionary-like structure, poses unique chall
Externí odkaz:
http://arxiv.org/abs/2408.16803
Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task. Existing equivariant Graph Neural Network (GNN) based methods have encapsulated the symmetry of physics, \emph{e.g.}, translations, rotations, etc,
Externí odkaz:
http://arxiv.org/abs/2405.12868
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses. In this work, we delve into the robustness analysis of
Externí odkaz:
http://arxiv.org/abs/2311.14934
Polynomial graph filters have been widely used as guiding principles in the design of Graph Neural Networks (GNNs). Recently, the adaptive learning of the polynomial graph filters has demonstrated promising performance for modeling graph signals on b
Externí odkaz:
http://arxiv.org/abs/2307.07956
The existing research on robust Graph Neural Networks (GNNs) fails to acknowledge the significance of directed graphs in providing rich information about networks' inherent structure. This work presents the first investigation into the robustness of
Externí odkaz:
http://arxiv.org/abs/2306.02002
Publikováno v:
In Applied Catalysis B: Environment and Energy January 2024 340
