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Siyuan Duan

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

CoPINN: Cognitive Physics-Informed Neural Networks

Siyuan Duan
Wenyuan Wu
Peng Hu 0002
Zhenwen Ren
Dezhong Peng
Yuan Sun 0016

Physics-informed neural networks (PINNs) aim to constrain the outputs and gradients of deep learning models to satisfy specified governing physics equations, which have demonstrated significant potential for solving partial differential equations (PDEs). Although existing PINN methods have achieved pleasing performance, they always treat both easy and hard sample points indiscriminately, especially ones in the physical boundaries. This easily causes the PINN model to fall into undesirable local minima and unstable learning, thereby resulting in an Unbalanced Prediction Problem (UPP). To deal with this daunting problem, we propose a novel framework named Cognitive Physical Informed Neural Network (CoPINN) that imitates the human cognitive learning manner from easy to hard. Specifically, we first employ separable subnetworks to encode independent one-dimensional coordinates and apply an aggregation scheme to generate multi-dimensional predicted physical variables. Then, during the training phase, we dynamically evaluate the difficulty of each sample according to the gradient of the PDE residuals. Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary regions. Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions.

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ICML Conference 2025 Conference Paper

Deep Fuzzy Multi-view Learning for Reliable Classification

Siyuan Duan
Yuan Sun 0016
Dezhong Peng
Guiduo Duan
Xi Peng 0001
Peng Hu 0002

Multi-view learning methods primarily focus on enhancing decision accuracy but often neglect the uncertainty arising from the intrinsic drawbacks of data, such as noise, conflicts, etc. To address this issue, several trusted multi-view learning approaches based on the Evidential Theory have been proposed to capture uncertainty in multi-view data. However, their performance is highly sensitive to conflicting views, and their uncertainty estimates, which depend on the total evidence and the number of categories, often underestimate uncertainty for conflicting multi-view instances due to the neglect of inherent conflicts between belief masses. To accurately classify conflicting multi-view instances and precisely estimate their intrinsic uncertainty, we present a novel Deep Fuzzy Multi-View Learning ( FUML ) method. Specifically, FUML leverages Fuzzy Set Theory to model the outputs of a classification neural network as fuzzy memberships, incorporating both possibility and necessity measures to quantify category credibility. A tailored loss function is then proposed to optimize the category credibility. To further enhance uncertainty estimation, we propose an entropy-based uncertainty estimation method leveraging category credibility. Additionally, we develop a Dual Reliable Multi-view Fusion (DRF) strategy that accounts for both view-specific uncertainty and inter-view conflict to mitigate the influence of conflicting views in multi-view fusion. Extensive experiments demonstrate that our FUML achieves state-of-the-art performance in terms of both accuracy and reliability.

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AAAI Conference 2025 Conference Paper

Noisy Label Calibration for Multi-View Classification

Shilin Xu
Yuan Sun
Xingfeng Li
Siyuan Duan
Zhenwen Ren
Zheng Liu
Dezhong Peng

In recent years, multi-view learning has aroused extensive research passion. Most existing multi-view learning methods often rely on well-annotations to improve decision accuracy. However, noise labels are ubiquitous in multi-view data due to imperfect annotations. To deal with this problem, we propose a novel noisy label calibration method (NLC) for multi-view classification to resist the negative impact of noisy labels. Specifically, to capture consensus information from multiple views, we employ max-margin rank loss to reduce the heterogeneous gap. Subsequently, we evaluate the confidence scores to enrich predictions associated with noise instances according to all reliable neighbors. Further, we propose Label Noise Detection (LND) to separate multi-view data into a clean or noisy subset, and propose Label Calibration Learning (LCL) to correct noisy instances. Finally, we adopt the cross-entropy loss to achieve multi-view classification. Extensive experiments on six datasets validate that our method outperforms eight state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks

Xuyang Wang
Siyuan Duan
Qizhi Li
Guiduo Duan
Yuan Sun
Dezhong Peng

Trustworthy multi-view learning has attracted extensive attention because evidence learning can provide reliable uncertainty estimation to enhance the credibility of multi-view predictions. Existing trusted multi-view learning methods implicitly assume that multi-view data is secure. However, in safety-sensitive applications such as autonomous driving and security monitoring, multi-view data often faces threats from adversarial perturbations, thereby deceiving or disrupting multi-view models. This inevitably leads to the adversarial unreliability problem (AUP) in trusted multi-view learning. To overcome this tricky problem, we propose a novel multi-view learning framework, namely Reliable Disentanglement Multi-view Learning (RDML). Specifically, we first propose evidential disentanglement learning to decompose each view into clean and adversarial parts under the guidance of corresponding evidences, which is extracted by a pretrained evidence extractor. Then, we employ the feature recalibration module to mitigate the negative impact of adversarial perturbations and extract potential informative features from them. Finally, to further ignore the irreparable adversarial interferences, a view-level evidential attention mechanism is designed. Extensive experiments on multi-view classification tasks with adversarial attacks show that RDML outperforms the state-of-the-art methods by a relatively large margin. Our code is available at https: //github. com/Willy1005/2025-IJCAI-RDML.

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