Despite its widespread adoption as the prominent neural architecture, the
Transformer has spurred several independent lines of work to address its
limitations. One such approach is selective state space models, which have
demonstrated promising results for language modelling. However, their
feasibility for learning self-supervised, general-purpose audio representations
is yet to be investigated. This work proposes Audio Mamba, a selective state
space model for learning general-purpose audio representations from randomly
masked spectrogram patches through self-supervision. Empirical results on ten
diverse audio recognition downstream tasks show that the proposed models,
pretrained on the AudioSet dataset, consistently outperform comparable
self-supervised audio spectrogram transformer (SSAST) baselines by a
considerable margin and demonstrate better performance in dataset size,
sequence length and model size comparisons.

提出了一种名为 Audio Mamba 的选择性状态空间模型，通过自我监督学习从随机遮罩的频谱图补丁中学习通用的音频表示。实证结果表明，预训练于 AudioSet 数据集的该模型在十个不同的音频识别任务中持续优于可比较的自我监督音频谱图 Transformer (SSAST) 基线，表现出更好的数据集大小、序列长度和模型大小的对比性能。

音频蟒蛇：用于自监督音频表示的选择性状态空间

Audio Mamba: Selective State Spaces for Self-Supervised Audio  Representations

We propose a heterogeneous graph mamba network (HGMN) as the first
exploration in leveraging the selective state space models (SSSMs) for
heterogeneous graph learning. Compared with the literature, our HGMN overcomes
two major challenges: (i) capturing long-range dependencies among heterogeneous
nodes and (ii) adapting SSSMs to heterogeneous graph data. Our key contribution
is a general graph architecture that can solve heterogeneous nodes in
real-world scenarios, followed an efficient flow. Methodologically, we
introduce a two-level efficient tokenization approach that first captures
long-range dependencies within identical node types, and subsequently across
all node types. Empirically, we conduct comparisons between our framework and
19 state-of-the-art methods on the heterogeneous benchmarks. The extensive
comparisons demonstrate that our framework outperforms other methods in both
the accuracy and efficiency dimensions.

我们提出了一种异构图曼巴网络 (HGMN)，它是利用选择性状态空间模型 (SSSMs) 进行异构图学习的首次探索。相比文献，我们的 HGMN 克服了两个主要挑战：(i) 捕捉异构节点之间的长程依赖和 (ii) 将 SSSMs 针对异构图数据进行适应。我们的主要贡献是提出了一个通用的图结构，可以解决现实场景中的异构节点，并进行高效的流程。在方法上，我们引入了一种两级高效的标记化方法，首先在相同节点类型内捕捉长程依赖，然后在所有节点类型之间进行捕捉。在实证方面，我们在异构基准测试上将我们的框架与其他 19 个最先进的方法进行了比较。广泛的比较表明，我们的框架在准确性和效率维度上表现优于其他方法。

HeteGraph-Mamba: 基于选择性状态空间模型的异构图学习

HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space  Model

Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous,
and non-stationary, leading to the continuous challenge of spatial-temporal
graph learning. In the past few years, various GNN-based methods have been
proposed to solely focus on mimicking the relationships among node individuals
of the STG network, ignoring the significance of modeling the intrinsic
features that exist in STG system over time. In contrast, modern Selective
State Space Models (SSSMs) present a new approach which treat STG Network as a
system, and meticulously explore the STG system's dynamic state evolution
across temporal dimension. In this work, we introduce Spatial-Temporal Graph
Mamba (STG-Mamba) as the first exploration of leveraging the powerful selective
state space models for STG learning by treating STG Network as a system, and
employing the Graph Selective State Space Block (GS3B) to precisely
characterize the dynamic evolution of STG networks. STG-Mamba is formulated as
an Encoder-Decoder architecture, which takes GS3B as the basic module, for
efficient sequential data modeling. Furthermore, to strengthen GNN's ability of
modeling STG data under the setting of SSSMs, we propose Kalman Filtering Graph
Neural Networks (KFGN) for adaptive graph structure upgrading. KFGN smoothly
fits in the context of selective state space evolution, and at the same time
keeps linear complexity. Extensive empirical studies are conducted on three
benchmark STG forecasting datasets, demonstrating the performance superiority
and computational efficiency of STG-Mamba. It not only surpasses existing
state-of-the-art methods in terms of STG forecasting performance, but also
effectively alleviate the computational bottleneck of large-scale graph
networks in reducing the computational cost of FLOPs and test inference time.

基于选择性状态空间模型，该研究提出了一种名为 STG-Mamba 的时空图学习方法，借助图选择性状态空间块（GS3B）刻画了 STG 网络的动态演化，并引入了 Kalman 滤波图神经网络（KFGN）来提升 GNN 在 STG 数据建模中的能力。大量实证研究表明了 STG-Mamba 在预测性能和计算效率方面的优越性。