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Yan Peng

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6 papers
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6

NeurIPS Conference 2025 Conference Paper

Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes

  • Wei Wu
  • Xuan Tan
  • Yan Peng
  • Ling Chen
  • Fangfang Li
  • Chuan Luo

Signed networks can reflect more complex connections through positive and negative edges, and cost-effective signed network sketching can significantly benefit an important link sign prediction task in the era of big data. Existing signed network embedding algorithms mainly learn node representation in the Graph Neural Network (GNN) framework with the balance theory. However, the node-wise representation learning methods either limit the representational power because they primarily rely on node pairwise relationship in the network, or suffer from severe efficiency issues. Recent research has explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by that, we propose EdgeSketch+, a simple and effective edge embedding algorithm beyond traditional node-centric modeling that directly represents edges as low-dimensional vectors without transitioning from node embeddings. The proposed approach maintains a good balance between accuracy and efficiency by exploiting the Locality Sensitive Hashing (LSH) technique to swiftly capture the higher-order information derived from the simplicial complex in a manner of no learning processes. Experiments show that EdgeSketch+ matches state-of-the-art accuracy while significantly reducing runtime, achieving speedups of up to $546. 07\times$ compared to GNN-based methods.

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EAAI Journal 2025 Journal Article

Enhanced underwater acoustic target recognition using parallel dual-branch network with attention mechanism

  • Jingpu Xu
  • Xiaowei Li
  • Dan Zhang
  • Yaoran Chen
  • Yan Peng
  • Wenhu Liu

Ship-radiated noise serves as a crucial source of underwater acoustic signals for vessel classification, but its identification is often hindered by environmental variability and internal noise interference. To address these challenges, we propose a dual-branch Residual Attention-Long Short-Term Memory (ResA-LSTM) network for underwater acoustic target recognition. The proposed model integrates a Residual Attention (ResA) branch to extract spatial features and a Bidirectional Long Short-Term Memory (Bi-LSTM) branch to capture long-term temporal dependencies from Mel spectrograms. The ResA module incorporates attention mechanisms and residual connections to enhance feature selection and improve robustness in noisy environments. Evaluations conducted on two public datasets, ShipsEar and DeepShip, demonstrate the effectiveness of our approach, achieving classification accuracies of 98. 55 % and 99. 31 %, respectively. Sensitivity analysis further confirms the model's ability to handle long-duration acoustic sequences, highlighting its potential for practical deployment in real-world underwater recognition tasks.

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EAAI Journal 2025 Journal Article

Global–local adaptive resampling strategy for enhancing the performance of Physics-informed neural networks

  • Lei Gao
  • Dan Zhang
  • Yaoran Chen
  • Xiaowei Li
  • Chunxin Li
  • Yan Peng

In recent years, the rapid development of artificial intelligence (AI) has brought innovative solutions to the field of engineering. Physical-informed neural networks (PINNs) have provided a new paradigm for solving partial differential equations (PDEs). However, PINNs are extremely sensitive to the number and distribution of collocation points. Insufficient collocation points or uneven distribution may lead can lead to solution failure. To address this issue, this study proposes a global–local adaptive resampling strategy (GLAR) that combines Monte Carlo integration with PINNs. During the training process, the collocation points are resampled in both global and local regions according to the Monte Carlo integral values to improve the accuracy of the model. Numerical experiments show that GLAR-PINNs is comparable to existing resampling methods when dealing with linear PDEs (such as the Diffusion and Wave equations). When it comes to solving nonlinear PDEs (such as the Burgers, Korteweg–de Vries, and Allen–Cahn equations), the accuracy is improved has been improved by 14. 65 times, 38. 04 times, and 7. 14 times, respectively. In addition, we applied this strategy to reconstruct the flow field around a two-dimensional triangular cylinder, and the relative error of the reconstructed velocity field was less than 0. 001. This significantly promoting significantly promotes the application and development of artificial intelligence in the engineering field.

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EAAI Journal 2025 Journal Article

Plug-and-play dynamic optimization for three-dimensional Gaussian generation

  • Qixuan Li
  • Haoyang Li
  • Chao Wang
  • Yang Zhou
  • Yan Peng

Recent advancements in Three-Dimensional (3D) asset generation have demonstrated remarkable progress in generation efficiency, enabling transformative applications across creative industries and mission-critical domains including autonomous systems. Current 3D asset generation primarily employs Score Distillation Sampling (SDS) to derive 3D priors from Two-Dimensional (2D) diffusion models. While this contribution ensures high generation quality, it is time-consuming. Recent methods have utilized 3D Gaussian Splatting for image rendering, which, despite enhancing generation speed, compromised on quality. Our method aims to balance the quality and speed of 3D asset generation by designing a plug-and-play optimization process that combines the strengths of both methods. We propose a rapid 3D Gaussian generation framework that begins with constructing a pipeline to generate multi-view images from text input using pre-trained generative models. Then our method utilizes 3D Gaussian Splatting for quick 3D asset initialization and subsequently performs detail optimization using Gaussian Filter and SDS-based 2D diffusion model optimizer. Additionally, we have optimized the loss function for 3D Gaussian Splatting and ensured the entire optimization process is plug-and-play, offering high generation quality and speed. Our method demonstrates strong adaptability in representative single-object 3D Gaussian generation tasks, indicating promising generalization potential. Achieving high-quality 3D generation on a single Graphics Processing Unit (GPU), our framework outperforms most popular optimization-based models in generation speed (5 × speedup +). Furthermore, when juxtaposed with the latest inference-based models, our optimization architecture offers a notable enhancement in generation quality (Contrastive Language-Image Pre-Training Score 33. 8 vs. 27. 3) within an acceptable amount of time.

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IROS Conference 2025 Conference Paper

TVFET-VD: Time-Varying Formation Encircling and Tracking Control Based on Visual Detection

  • Guang Yang
  • Juntong Qi
  • Mingming Wang
  • Hailong Huang 0001
  • Yan Peng
  • Chong Wu 0004
  • Yuan Ping 0001

This paper proposes a whole process method of multi-quadrotors from detecting and locating to encircle and track targets. The reconnaissance quadrotor realizes accurate target detection based on the one-stage target detector of convolutional neural network. Then, based on a pinhole camera projection model, the target is located from the 2D pixel coordinates to 3D North East Down(NED) coordinates world. Finally, the hunter quadrotors realize the target encircling and the time-varying formation tracking based on the consensus theory. At the same time, we prove the stability of the time-varying formation tracking control. We built a multiple quadrotors platform composed of one reconnaissance quadrotor and four hunter quadrotors, and deployed the method on the platform to conduct a series of experiments with a minibus as the target for validation. The results indicate that reconnaissance quadrotor can accurately detect target and have small localization errors in the north and east directions. Hunter quadrotors can encircle and track targets in time-varying formation based on target information provided by reconnaissance quadrotor. Experiments have demonstrated that the method achieves high-speed and accurate target encirclement.

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NeurIPS Conference 2021 Conference Paper

Dual-stream Network for Visual Recognition

  • Mingyuan Mao
  • Peng Gao
  • Renrui Zhang
  • Honghui Zheng
  • Teli Ma
  • Yan Peng
  • Errui Ding
  • Baochang Zhang

Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input images. In this paper, we present a generic Dual-stream Network (DS-Net) to fully explore the representation capacity of local and global pattern features for image classification. Our DS-Net can simultaneously calculate fine-grained and integrated features and efficiently fuse them. Specifically, we propose an Intra-scale Propagation module to process two different resolutions in each block and an Inter-Scale Alignment module to perform information interaction across features at dual scales. Besides, we also design a Dual-stream FPN (DS-FPN) to further enhance contextual information for downstream dense predictions. Without bells and whistles, the proposed DS-Net outperforms DeiT-Small by 2. 4\% in terms of top-1 accuracy on ImageNet-1k and achieves state-of-the-art performance over other Vision Transformers and ResNets. For object detection and instance segmentation, DS-Net-Small respectively outperforms ResNet-50 by 6. 4\% and 5. 5 \% in terms of mAP on MSCOCO 2017, and surpasses the previous state-of-the-art scheme, which significantly demonstrates its potential to be a general backbone in vision tasks. The code will be released soon.

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v2026.09.13