DeciMamba: Exploring the Length Extrapolation Potential of Mamba

• URL: http://arxiv.org/abs/2406.14528v1
• Date: Thu, 20 Jun 2024 17:40:18 GMT
• Title: DeciMamba: Exploring the Length Extrapolation Potential of Mamba
• Authors: Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, Raja Giryes,
• Abstract summary: We introduce DeciMamba, a context-extension method specifically designed for Mamba. We show that DeciMamba can extrapolate context lengths 25x longer than the ones seen during training, and does so without utilizing additional computational resources.
• Score: 89.07242846058023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-range sequence processing poses a significant challenge for Transformers due to their quadratic complexity in input length. A promising alternative is Mamba, which demonstrates high performance and achieves Transformer-level capabilities while requiring substantially fewer computational resources. In this paper we explore the length-generalization capabilities of Mamba, which we find to be relatively limited. Through a series of visualizations and analyses we identify that the limitations arise from a restricted effective receptive field, dictated by the sequence length used during training. To address this constraint, we introduce DeciMamba, a context-extension method specifically designed for Mamba. This mechanism, built on top of a hidden filtering mechanism embedded within the S6 layer, enables the trained model to extrapolate well even without additional training. Empirical experiments over real-world long-range NLP tasks show that DeciMamba can extrapolate to context lengths that are 25x times longer than the ones seen during training, and does so without utilizing additional computational resources. We will release our code and models.

Related papers

• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• SIGMA: Selective Gated Mamba for Sequential Recommendation [56.85338055215429]
  Mamba, a recent advancement, has exhibited exceptional performance in time series prediction. We introduce a new framework named Selective Gated Mamba ( SIGMA) for Sequential Recommendation. Our results indicate that SIGMA outperforms current models on five real-world datasets.
  arXiv  Detail & Related papers  (2024-08-21T09:12:59Z)
• FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecasting [6.152779144421304]
  We introduce a novel framework named FMamba for multivariate time-series forecasting (MTSF) Technically, we first extract the temporal features of the input variables through an embedding layer, then compute the dependencies among input variables via the fast-attention module. We use Mamba to selectively deal with the input features and further extract the temporal dependencies of the variables through the multi-layer perceptron block (MLP-block) Finally, FMamba obtains the predictive results through the projector, a linear layer.
  arXiv  Detail & Related papers  (2024-07-20T09:14:05Z)
• MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting [12.08746904573603]
  Mamba, based on selective state space models (SSMs), has emerged as a competitive alternative to Transformer. We propose four targeted improvements, leading to MambaTS. Experiments conducted on eight public datasets demonstrate that MambaTS achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-05-26T05:50:17Z)
• Bi-Mamba+: Bidirectional Mamba for Time Series Forecasting [5.166854384000439]
  Long-term time series forecasting (LTSF) provides longer insights into future trends and patterns. Recently, a new state space model (SSM) named Mamba is proposed. With the selective capability on input data and the hardware-aware parallel computing algorithm, Mamba has shown great potential in balancing predicting performance and computational efficiency.
  arXiv  Detail & Related papers  (2024-04-24T09:45:48Z)
• Is Mamba Effective for Time Series Forecasting? [30.85990093479062]
  We propose a Mamba-based model named Simple-Mamba (S-Mamba) for time series forecasting. Specifically, we tokenize the time points of each variate autonomously via a linear layer. Experiments on thirteen public datasets prove that S-Mamba maintains low computational overhead and achieves leading performance.
  arXiv  Detail & Related papers  (2024-03-17T08:50:44Z)
• MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology [10.933433327636918]
  Multiple Instance Learning (MIL) has emerged as a dominant paradigm to extract discriminative feature representations within Whole Slide Images (WSIs) in computational pathology. In this paper, we incorporate the Selective Scan Space State Sequential Model (Mamba) in Multiple Instance Learning (MIL) for long sequence modeling with linear complexity. Our proposed framework performs favorably against state-of-the-art MIL methods.
  arXiv  Detail & Related papers  (2024-03-11T15:17:25Z)
• Is Mamba Capable of In-Context Learning? [63.682741783013306]
  State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL) This work provides empirical evidence that Mamba, a newly proposed state space model, has similar ICL capabilities.
  arXiv  Detail & Related papers  (2024-02-05T16:39:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.