{"total":53,"rows":[{"id":"2312.06635","title":"Gated Linear Attention Transformers with Hardware-Efficient Training","created":"2023-12-11","cited_by_count":514,"area":"ssm"},{"id":"2407.04620","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","created":"2024-07-05","cited_by_count":355,"area":"ssm"},{"id":"2406.07522","title":"Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling","created":"2024-06-11","cited_by_count":174,"area":"ssm"},{"id":"2408.15237","title":"The Mamba in the Llama: Distilling and Accelerating Hybrid Models","created":"2024-08-27","cited_by_count":122,"area":"ssm"},{"id":"2402.04248","title":"Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks","created":"2024-02-06","cited_by_count":105,"area":"ssm"},{"id":"2403.01590","title":"The Hidden Attention of Mamba Models","created":"2024-03-03","cited_by_count":91,"area":"ssm"},{"id":"2402.05892","title":"Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data","created":"2024-02-08","cited_by_count":84,"area":"ssm"},{"id":"2403.15360","title":"SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series","created":"2024-03-22","cited_by_count":82,"area":"ssm"},{"id":"2404.16112","title":"Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges","created":"2024-04-24","cited_by_count":64,"area":"ssm"},{"id":"2407.14207","title":"Longhorn: State Space Models are Amortized Online Learners","created":"2024-07-19","cited_by_count":59,"area":"ssm"},{"id":"2404.15956","title":"A Survey on Visual Mamba","created":"2024-04-24","cited_by_count":50,"area":"ssm"},{"id":"2406.14528","title":"DeciMamba: Exploring the Length Extrapolation Potential of Mamba","created":"2024-06-20","cited_by_count":49,"area":"ssm"},{"id":"2407.09941","title":"Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers","created":"2024-07-13","cited_by_count":44,"area":"ssm"},{"id":"2403.19888","title":"MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection","created":"2024-03-29","cited_by_count":40,"area":"ssm"},{"id":"2408.01129","title":"A Survey of Mamba","created":"2024-08-02","cited_by_count":39,"area":"ssm"},{"id":"2412.16178","title":"Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs","created":"2024-12-09","cited_by_count":39,"area":"ssm"},{"id":"2409.03231","title":"State-space models are accurate and efficient neural operators for dynamical systems","created":"2024-09-05","cited_by_count":28,"area":"ssm"},{"id":"2404.13222","title":"Vim4Path: Self-Supervised Vision Mamba for Histopathology Images","created":"2024-04-20","cited_by_count":19,"area":"ssm"},{"id":"2501.00658","title":"Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing","created":"2024-12-31","cited_by_count":18,"area":"ssm"},{"id":"2509.19633","title":"Mamba Modulation: On the Length Generalization of Mamba","created":"2025-09-23","cited_by_count":16,"area":"ssm"},{"id":"2402.10211","title":"Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling","created":"2024-02-15","cited_by_count":15,"area":"ssm"},{"id":"2404.07645","title":"Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos","created":"2024-04-11","cited_by_count":13,"area":"ssm"},{"id":"2405.16504","title":"Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation","created":"2024-05-26","cited_by_count":11,"area":"ssm"},{"id":"2412.20386","title":"PTQ4VM: Post-Training Quantization for Visual Mamba","created":"2024-12-29","cited_by_count":10,"area":"ssm"},{"id":"2505.21717","title":"Parallelization of Non-linear State-Space Models: Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling","created":"2025-05-27","cited_by_count":10,"area":"ssm"},{"id":"2503.18970","title":"Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba","created":"2025-03-22","cited_by_count":9,"area":"ssm"},{"id":"2505.15105","title":"Mechanistic evaluation of Transformers and state space models","created":"2025-05-21","cited_by_count":9,"area":"ssm"},{"id":"2402.15993","title":"Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation","created":"2024-02-25","cited_by_count":7,"area":"ssm"},{"id":"2407.06136","title":"Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning","created":"2024-07-08","cited_by_count":7,"area":"ssm"},{"id":"2505.08299","title":"Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments","created":"2025-05-13","cited_by_count":6,"area":"ssm"},{"id":"2403.18063","title":"Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series Analysis","created":"2024-03-26","cited_by_count":5,"area":"ssm"},{"id":"2502.01473","title":"Generalization Error Analysis for Selective State-Space Models Through the Lens of Attention","created":"2025-02-03","cited_by_count":5,"area":"ssm"},{"id":"2410.16020","title":"START: A Generalized State Space Model with Saliency-Driven Token-Aware Transformation","created":"2024-10-21","cited_by_count":4,"area":"ssm"},{"id":"2410.18572","title":"Taipan: Efficient and Expressive State Space Language Models with Selective Attention","created":"2024-10-24","cited_by_count":4,"area":"ssm"},{"id":"2412.10338","title":"XYScanNet: A State Space Model for Single Image Deblurring","created":"2024-12-13","cited_by_count":4,"area":"ssm"},{"id":"2309.08551","title":"Augmenting conformers with structured state-space sequence models for online speech recognition","created":"2023-09-15","cited_by_count":3,"area":"ssm"},{"id":"2410.15678","title":"Revealing and Mitigating the Local Pattern Shortcuts of Mamba","created":"2024-10-21","cited_by_count":3,"area":"ssm"},{"id":"2411.00151","title":"NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs","created":"2024-10-31","cited_by_count":3,"area":"ssm"},{"id":"2602.22719","title":"Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks","created":"2026-02-26","cited_by_count":3,"area":"ssm"},{"id":"2604.14191","title":"Attention to Mamba: A Recipe for Cross-Architecture Distillation","created":"2026-04-01","cited_by_count":3,"area":"ssm"},{"id":"2606.30461","title":"MuonSSM: Orthogonalizing State Space Models for Sequence Modeling","created":"2026-06-29","cited_by_count":3,"area":"ssm"},{"id":"2411.12603","title":"STREAM: A Universal State-Space Model for Sparse Geometric Data","created":"2024-11-19","cited_by_count":2,"area":"ssm"},{"id":"2507.06204","title":"Differential Mamba","created":"2025-07-08","cited_by_count":2,"area":"ssm"},{"id":"2403.10696","title":"On the low-shot transferability of [V]-Mamba","created":"2024-03-15","cited_by_count":1,"area":"ssm"},{"id":"2406.16722","title":"Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba","created":"2024-06-24","cited_by_count":1,"area":"ssm"},{"id":"2411.08909","title":"Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers","created":"2024-10-29","cited_by_count":1,"area":"ssm"},{"id":"2603.20997","title":"When Does Content-Based Routing Work? Representation Requirements for Selective Attention in Hybrid Sequence Models","created":"2026-03-22","cited_by_count":1,"area":"ssm"},{"id":"2502.19429","title":"scMamba: A Pre-Trained Model for Single-Nucleus RNA Sequencing Analysis in Neurodegenerative Disorders","created":"2025-02-12","cited_by_count":0,"area":"ssm"},{"id":"2509.18691","title":"An overview of neural architectures for self-supervised audio representation learning from masked spectrograms","created":"2025-09-23","cited_by_count":0,"area":"ssm"},{"id":"2512.23068","title":"Breaking the Memory Wall: Exact Analytical Differentiation via Tiled Operator-Space Evolution","created":"2025-12-28","cited_by_count":0,"area":"ssm"},{"id":"2603.12414","title":"SpectralGuard: Detecting Memory Collapse Attacks in State Space Models","created":"2026-03-12","cited_by_count":0,"area":"ssm"},{"id":"2605.11300","title":"Can Graphs Help Vision SSMs See Better?","created":"2026-05-11","cited_by_count":0,"area":"ssm"},{"id":"2608.18592","title":"Infrared Universality of Collective Dynamics across Transformer and State-Space Architectures","created":"2026-08-19","cited_by_count":0,"area":"ssm"}],"next_cursor":null}