Selective State Space Models
PulseAugur coverage of Selective State Space Models — every cluster mentioning Selective State Space Models across labs, papers, and developer communities, ranked by signal.
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Selective State Space Models Show Token Consensus Similar to Transformers
Researchers have explored the dynamics of selective state space models (SSMs), comparing their token aggregation mechanisms to those in transformers. By analyzing SSMs from a dynamical systems perspective, they found th…
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New research explores advanced techniques for continual learning in AI models · 8 sources tracked
Researchers are developing new methods for continual learning, which aims to enable AI models to learn new information without forgetting previously acquired knowledge. One approach, "Class Incremental Continual Learnin…
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KHiM-Mamba architecture enhances pathology image analysis with knowledge integration
Researchers have developed KHiM-Mamba, a novel architecture designed to improve whole slide image analysis in pathology. This new model integrates pathology knowledge directly into the Mamba selective state-space model,…
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Vision Mamba vs. MambaOut: Decoding distinct visual encoding strategies
A new research paper investigates the differing encoding strategies of Vision Mamba (VMamba) and MambaOut models, which both utilize selective state space models (SSMs) as alternatives to traditional self-attention for …
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New research analyzes stability properties of Mamba-like selective SSMs
Researchers have published a paper on the regularity and stability properties of Selective State-Space Models (SSMs), a type of model used in long-sequence modeling, with Mamba being a prominent example. The study appli…
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Mamba-FSCIL: Selective State Space Models for Few-Shot Class-Incremental Learning
Researchers have developed Mamba-FSCIL, a novel approach to few-shot class-incremental learning that utilizes Selective State Space Models (SSMs). This method addresses the challenge of balancing static and dynamic arch…