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State Space Models

Papers

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Showing 401–450 of 923 papers

TitleStatusHype
From S4 to Mamba: A Comprehensive Survey on Structured State Space Models—0
Exemplar-Free Continual Learning for State Space Models—0
Event Signal Filtering via Probability Flux Estimation—0
Comparative Study of State-based Neural Networks for Virtual Analog Audio Effects Modeling—0
EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction—0
Event-based Video Super-Resolution via State Space Models—0
Comba: Improving Bilinear RNNs with Closed-loop Control—0
Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models—0
Estimation of black bear abundance by management zone in Wisconsin—0
Entropy Analysis of Financial Time Series—0
CMamba: Learned Image Compression with State Space Models—0
A Survey on Mamba Architecture for Vision Applications—0
A Survey of Transformer Enabled Time Series Synthesis—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
A study on fault diagnosis in nonlinear dynamic systems with uncertainties—0
Enhanced Prediction of Multi-Agent Trajectories via Control Inference and State-Space Dynamics—0
Empirical Evaluation of Knowledge Distillation from Transformers to Subquadratic Language Models—0
Closed-form Inference and Prediction in Gaussian Process State-Space Models—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
Embedding Information onto a Dynamical System—0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models—0
Element-wise Attention Is All You Need—0
Chimera: A Block-Based Neural Architecture Search Framework for Event-Based Object Detection—0
Efficient variational approximations for state space models—0
Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments—0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework—0
A Dynamically Controlled Recurrent Neural Network for Modeling Dynamical Systems—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model—0
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging—0
Machine Learning Models for Dengue Forecasting in Singapore—0
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences—0
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline—0
LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing—0
Look Every Frame All at Once: Video-Ma^2mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing—0
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters—0
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory—0
LongSSM: On the Length Extension of State-space Models in Language Modelling—0
Efficient learning of hidden state LTI state space models of unknown order—0
Efficient Image Compression Using Advanced State Space Models—0
Long-Context State-Space Video World Models—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
A Sparse Bayesian Deep Learning Approach for Identification of Cascaded Tanks Benchmark—0
Log-Linear Attention—0
Efficient Estimation of State-Space Mixed-Frequency VARs: A Precision-Based Approach—0
Localized active learning of Gaussian process state space models—0
LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws—0
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution—0
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