SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series

• URL: http://arxiv.org/abs/2403.15360v2
• Date: Wed, 24 Apr 2024 18:19:21 GMT
• Title: SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
• Authors: Badri N. Patro, Vijay S. Agneeswaran,
• Abstract summary: We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. We show that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers.
• Score: 2.4379295576598436
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 and others (Hippo, Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address the above issues to help handle longer sequence lengths. Mamba, while being the state-of-the-art SSM, has a stability issue when scaled to large networks for computer vision datasets. We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. Extensive performance studies across image and time-series benchmarks demonstrate that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers. Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet and transfer learning benchmarks such as Stanford Car and Flower as well as task learning benchmarks as well as seven time series benchmark datasets. The project page is available on this website ~\url{https://github.com/badripatro/Simba}.

Related papers

• Mamba-CL: Optimizing Selective State Space Model in Null Space for Continual Learning [54.19222454702032]
  Continual Learning aims to equip AI models with the ability to learn a sequence of tasks over time, without forgetting previously learned knowledge. State Space Models (SSMs) have achieved notable success in computer vision. We introduce Mamba-CL, a framework that continuously fine-tunes the core SSMs of the large-scale Mamba foundation model.
  arXiv  Detail & Related papers  (2024-11-23T06:36:16Z)
• TIMBA: Time series Imputation with Bi-directional Mamba Blocks and Diffusion models [0.0]
  We propose replacing time-oriented Transformers with State-Space Models (SSM) We develop a model that integrates SSM, Graph Neural Networks, and node-oriented Transformers to achieve enhanced representations.
  arXiv  Detail & Related papers  (2024-10-08T11:10:06Z)
• Bidirectional 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)
• Longhorn: State Space Models are Amortized Online Learners [51.10124201221601]
  State-space models (SSMs) offer linear decoding efficiency while maintaining parallelism during training. In this work, we explore SSM design through the lens of online learning, conceptualizing SSMs as meta-modules for specific online learning problems. We introduce a novel deep SSM architecture, Longhorn, whose update resembles the closed-form solution for solving the online associative recall problem.
  arXiv  Detail & Related papers  (2024-07-19T11:12:08Z)
• An Empirical Study of Mamba-based Language Models [69.74383762508805]
  Selective state-space models (SSMs) like Mamba overcome some shortcomings of Transformers. We present a direct comparison between 8B-context Mamba, Mamba-2, and Transformer models trained on the same datasets. We find that the 8B Mamba-2-Hybrid exceeds the 8B Transformer on all 12 standard tasks.
  arXiv  Detail & Related papers  (2024-06-12T05:25:15Z)
• Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling [70.94320930424331]
  We present Samba, a simple hybrid architecture that layer-wise combines Mamba, a selective State Space Model (SSM), with Sliding Window Attention (SWA) Samba selectively compresses a given sequence into recurrent hidden states while still maintaining the ability to precisely recall memories with the attention mechanism. As a linear-time sequence model, Samba enjoys a 3.73x higher throughput compared to Transformers with grouped-attention when processing user prompts of 128K length, and 3.64x speedup when generating 64K tokens with unlimited streaming.
  arXiv  Detail & Related papers  (2024-06-11T17:50:51Z)
• Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges [1.4408339076385341]
  State Space Models (SSMs) have emerged as promising alternatives for sequence modeling paradigms. This survey highlights diverse applications of SSMs across domains such as vision, video, audio, speech, language (especially long sequence modeling), medical (including genomics), chemical (like drug design), recommendation systems, and time series analysis.
  arXiv  Detail & Related papers  (2024-04-24T18:10:31Z)
• MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection [5.37935922811333]
  MambaMixer is a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels. As a proof of concept, we design Vision MambaMixer (ViM2) and Time Series MambaMixer (TSM2) architectures based on the MambaMixer block.
  arXiv  Detail & Related papers  (2024-03-29T00:05:13Z)
• PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
  We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks. Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
  arXiv  Detail & Related papers  (2024-02-16T14:56:13Z)
• BlackMamba: Mixture of Experts for State-Space Models [10.209192169793772]
  State-space models (SSMs) have recently demonstrated competitive performance to transformers at large-scale language modeling benchmarks. MoE models have shown remarkable performance while significantly reducing the compute and latency costs of inference. We present BlackMamba, a novel architecture that combines the Mamba SSM with MoE to obtain the benefits of both.
  arXiv  Detail & Related papers  (2024-02-01T07:15:58Z)
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces [31.985243136674146]
  Foundation models are almost universally based on the Transformer architecture and its core attention module. We identify that a key weakness of such models is their inability to perform content-based reasoning. We integrate these selective SSMs into a simplified end-to-end neural network architecture without attention or even blocks (Mamba) As a general sequence model backbone, Mamba achieves state-of-the-art performance across several modalities such as language, audio, and genomics.
  arXiv  Detail & Related papers  (2023-12-01T18:01:34Z)

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.