State Space Model
PulseAugur coverage of State Space Model — every cluster mentioning State Space Model across labs, papers, and developer communities, ranked by signal.
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StateSMix compressor uses Mamba SSMs and n-grams for online lossless compression
Researchers have developed StateSMix, a novel lossless compression algorithm that utilizes Mamba-style State Space Models (SSMs) combined with sparse n-gram context mixing. This system trains token-by-token on the data …
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ViM-Q enables efficient Vision Mamba model inference on FPGAs
Researchers have developed ViM-Q, a novel algorithm-hardware co-design specifically for accelerating Vision Mamba (ViM) model inference on FPGAs. This approach tackles challenges in quantizing dynamic activation outlier…
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NAKUL-Med model enhances medical signal analysis with dynamic kernels and spectral context
Researchers have developed NAKUL-Med, a novel spectral-graph state space model designed to enhance the analysis of multi-channel medical signals. This model addresses limitations in existing state space models by incorp…
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SSMProbe framework reveals importance of token order in visual representations
Researchers have developed SSMProbe, a new framework for analyzing visual representations in AI models. This method utilizes State Space Models (SSMs) to account for the critical role of token order, challenging the tra…
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Caracal architecture uses Fourier transforms for efficient long-sequence modeling
Researchers have introduced Caracal, a new architecture designed to improve the scalability of large language models for processing long sequences. Caracal replaces the computationally expensive attention mechanism with…
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L2RU introduces stable state-space models for machine learning and control
Researchers have introduced L2RU, a new class of structured state-space models (SSMs) designed to ensure input-output stability and robustness. This architecture integrates deep learning expressiveness with dynamical sy…
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New Mamba model variant enhances memory retention and bilinear computation
Researchers have introduced Bilinear Input Modulation (BIM) to enhance Selective State Space Models (SSMs), specifically Mamba, by incorporating state-input products. This augmentation allows for improved memory retenti…
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Mamba model offers Transformer-level performance with faster inference and longer context
Mamba, a new State Space Model (SSM), presents an alternative to the dominant Transformer architecture in AI. It aims to match Transformer performance and scaling laws while efficiently handling extremely long sequences…