arXiv:2409.10489
공식 코드 · Papers with Code
Flash STU: Fast Spectral Transform Units
원문 초록
This paper describes an efficient, open source PyTorch implementation of the Spectral Transform Unit. We investigate sequence prediction tasks over several modalities including language, robotics, and simulated dynamical systems. We find that for the same parameter count, the STU and its variants outperform the Transformer as well as other leading state space models across various modalities.
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Optimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics
Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow
Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks
토론
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