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1. Data-Driven Quadratic Modeling in the Loewner Framework from Input-Output Time-Domain Measurements NSTL国家科技图书文献中心

Karachalios, D. S. |  Gosea, I., V... -  《SIAM journal on applied dynamical systems》 - 2025,24(1) - 457~500 - 共44页

摘要: identification of low-order in-state dimension quadratic state |  nonlinear optimization techniques to infer quadratic | ) of the quasi-steady-state system response under | -space models with nontrivial stable equilibria, such | In this study, we present a purely data-driven
关键词: data-driven methods |  model reduction |  system identification |  Loewner framework |  quadratic state-space modeling |  Lorenz attractor |  viscous Burgers' equation |  Volterra series |  quadratic matrix |  vector equations

2. ZigMa: A DiT-style Zigzag Mamba Diffusion Model NSTL国家科技图书文献中心

Vincent Tao Hu |  Stefan Andreas Bauma...... -  《Computer Vision - ECCV 2024,Part XXI》 -  European Conference on Computer Vision - 2025, - 148~166 - 共19页

摘要: capability of a State-Space Model called Mamba to extend |  scalability and quadratic complexity issues, especially |  aim to leverage the long sequence modeling | The diffusion model has long been plagued by |  within transformer-based structures. In this study, we
关键词: Diffusion model |  State-space model |  Stochastic interpolants

3. Efficient Long-Range Context Modeling for Motion Forecasting with State Space Models NSTL国家科技图书文献中心

Zhiwei Dong |  Ran Ding... -  《Pattern Recognition,Part XXVI》 -  International Conference on Pattern Recognition - 2025, - 186~201 - 共16页

摘要: efficiency. Inspired by the recent success of state space |  heavily on modeling the long-range global context in |  inherent quadratic complexity of self-attention limits |  models with linear complexity in long sequence modeling |  effectiveness and efficiency in modeling long-range context
关键词: Motion forecasting |  State space models |  Autonomous driving |  Context modeling

4. Mamba-Based Light Field Super-Resolution with Efficient Subspace Scanning NSTL国家科技图书文献中心

Ruisheng Gao |  Zeyu Xiao... -  《Computer Vision - ACCV 2024,Part V》 -  Asian Conference on Computer Vision - 2025, - 421~437 - 共17页

摘要: selective state-space model, Mamba, has gained popularity | -resolution by effectively modeling long-range spatial | -angular correlations, but their quadratic complexity |  for its efficient long-range sequence modeling. In | Transformer-based methods have demonstrated
关键词: Light field |  Super-resolution |  State space model

5. HSIRMamba: An effective feature learning for hyperspectral image classification using residual Mamba NSTL国家科技图书文献中心

Arya R.K. |  Jain S.... -  《Image and vision computing》 - 2025,154(Feb.) - 1.1~1.12 - 共12页

摘要:. Fortunately, the newly developed state space model Mamba |  Transformer exhibits quadratic computational complexity |  Transformer-like modeling capabilities. Therefore, we |  competitive results compared to state-of-the-art methods. | © 2024 Elsevier B.V.Deep learning models have
关键词: Classification |  Feature extraction |  Hyperspectral image |  Mamba |  State space model

6. DemoireingMamba: Visual State Space Model for Image Demoireing NSTL国家科技图书文献中心

Chia-Hung Yeh |  Chen Lo -  《International Workshop on Advanced Imaging Technology (IWAIT) 2025》 -  International Workshop on Advanced Imaging Technology - 2025, - 1351003.1~1351003.6 - 共6页

摘要: quadratic computational complexity. Recently, the state |  results. While CNNs struggle with modeling remote |  space model (SSM) known as Mamba has emerged as a |  outperforms state-of-the-art methods, achieving superior | Moire effect is important in image processing
关键词: Moire pattern |  Deep learning |  Mamba |  Image restoration

7. Visual State Space Model for Image Super-Resolution NSTL国家科技图书文献中心

Zhang, Jinhang |  Gao, Min... -  《IEEE Transactions on Instrumentation and Measurement》 - 2025,74(Pt.1) - 5004208.1~5004208.8 - 共8页

摘要: create a new dense-residual state-space block (DRSSB |  Transformers suffer from the quadratic complexity of |  CNN and provides new advanced modeling capabilities |  complexity, thereby achieving state-of-the-art performance | Transformers and convolutional neural networks
关键词: Dense connection |  image super-resolution (SR) |  mamba |  state space model (SSM)

8. Self-prior Guided Mamba-UNet Networks for Medical Image Super-Resolution NSTL国家科技图书文献中心

Zexin Ji |  Beiji Zou... -  《Pattern Recognition,Part XI》 -  International Conference on Pattern Recognition - 2025, - 160~174 - 共15页

摘要: complexity. Recently, State Space Models (SSMs) especially |  calculation challenges due to their quadratic computational |  Mamba have emerged, capable of modeling long-range |  approach outperforms current state-of-the-art methods on | In this paper, we propose a self-prior guided
关键词: Medical imaging |  Super-Resolution |  State space models |  Mamba |  Unet |  Deep learning

9. Pocket convolution Mamba for brain tumor segmentation NSTL国家科技图书文献中心

Hao Zhang |  Jiashu Wang... -  《The Journal of Supercomputing》 - 2025,81(1) - 235.1~235.23 - 共23页

摘要: computational complexity. Recently, state space models (SSM |  long-range modeling, and although transformers can |  model at long distances, they have quadratic | In the field of brain tumor segmentation | , models based on CNNs and transformer have received a
关键词: Brain tumor segmentation |  State space models |  Feature complement |  Spatial channel attention

10. VGA: Hardware Accelerator for Scalable Long Sequence Model Inference NSTL国家科技图书文献中心

Seung Yul Lee |  Hyunseung Lee... -  《2024 57th IEEE/ACM International Symposium on Microarchitecture》 -  IEEE/ACM International Symposium on Microarchitecture - 2024, - 1444~1457 - 共14页

摘要: length. Recently, state space model (SSM)-based global | Effectively modeling relationships between |  state-of-the-art models for processing sequential data |  quadratic to the input sequence length, which often | , have been found to be effective for modeling long
关键词: Analytical models |  Convolution |  Fast Fourier transforms |  Computational modeling |  Graphics processing units |  Random access memory |  Bandwidth |  Transformers |  Data models |  System-on-chip
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