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State Space Models

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Showing 576–600 of 923 papers

TitleStatusHype
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues—0
Deep Variational Luenberger-type Observer for Stochastic Video Prediction—0
Optimal Continuous State POMDP Planning with Semantic Observations: A Variational Approach—0
UnHiPPO: Uncertainty-aware Initialization for State Space Models—0
Deep State Space Recurrent Neural Networks for Time Series Forecasting—0
OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction—0
Age-at-harvest models as monitoring and harvest management tools for Wisconsin carnivores—0
Deep State Space Models for Unconditional Word Generation—0
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models—0
Deep State Space Models for Time Series Forecasting—0
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models—0
PAC-Bayesian theory for stochastic LTI systems—0
PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training—0
Pairs Trading with Nonlinear and Non-Gaussian State Space Models—0
ParallelFlow: Parallelizing Linear Transformers via Flow Discretization—0
Parallelizing Autoregressive Generation with Variational State Space Models—0
Parallelizing Linear Transformers with the Delta Rule over Sequence Length—0
Parallel Sequence Modeling via Generalized Spatial Propagation Network—0
Deep Linear Hawkes Processes—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
UniMamba: Unified Spatial-Channel Representation Learning with Group-Efficient Mamba for LiDAR-based 3D Object Detection—0
Unlocking Guidance for Discrete State-Space Diffusion and Flow Models—0
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost—0
Particle filter-based Gaussian process optimisation for parameter inference—0
Particle Gibbs with Ancestor Sampling—0
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