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What is State Space Sequence Models (SSMs):** SSMs have emerged as a promising architecture for sequence modeling, combining aspects of recurrent neural networks (RNNs), convolutional neural networks (CNNs), and classical state space models. They offer efficient computation, either as a recurrence or convolution, with linear or near-linear scaling in sequence length. Despite their success in continuous signal data like audio and vision, SSMs have been less effective in modeling discrete and information-dense data such as text.
📌 Selective State Space Models (SSMs): The paper introduces a new class of selective SSMs, designed to achieve the modeling power of Transformers while maintaining linear scaling in sequence length. This involves a selection mechanism allowing the model to selectively propagate or forget information along the sequence length dimension, based on the input. The innovation also includes a hardware-aware parallel algorithm to address the computational challenges posed by making SSMs time- and input-variant.
📌 The selection mechanism in Mamba It allows filtering out irrelevant tokens, resetting the state to remove extraneous history, and managing how information propagates or interacts along the sequence dimension. This mechanism is also connected to the gating mechanisms of RNNs and can be applied to traditional RNNs or CNNs.
📌 Empirical Validation: Mamba's effectiveness is empirically validated in several domains, including language modeling, DNA sequencing, and audio waveform modeling. It outperforms previous state-of-the-art models in these domains and shows superior performance in both pretraining quality and domain-specific task performance.
▪️ Mamba: Linear-Time Sequence Modeling with Selective State Spaces
▪️ GitHub
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