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.
2 day(s) with sentiment data
-
TIDES model enhances irregular time series handling in selective SSMs
Researchers have introduced TIDES, a novel variant of selective state space models designed to improve handling of irregular time series data. Unlike existing selective SSMs like Mamba, which learn input-dependent time …
-
Deep learning and control theory merge in new arXiv paper
A new arXiv paper explores the intersection of deep learning and control theory, offering a new perspective on understanding neural network architectures and training algorithms. The paper details how concepts from cont…
-
New READ method offers faithful video attribution for state-space models
Researchers have developed READ, a novel parameter-free method for attributing predictions in selective state-space models, commonly used for long-sequence video analysis. Unlike existing methods that require multiple p…
-
New deep learning model accurately emulates optical audio compressors
Researchers have developed a novel method for accurately modeling the time-dependent responses of optical dynamic range compressors using deep neural networks. This approach leverages Selective State Space models, outpe…
-
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…
-
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…
-
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,…
-
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 …
-
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…
-
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…