Graph representation learning has become a crucial task in machine learning
and data mining due to its potential for modeling complex structures such as
social networks, chemical compounds, and biological systems. Spiking neural
networks (SNNs) have recently emerged as a promising alternative to traditional
neural networks for graph learning tasks, benefiting from their ability to
efficiently encode and process temporal and spatial information. In this paper,
we propose a novel approach that integrates attention mechanisms with SNNs to
improve graph representation learning. Specifically, we introduce an attention
mechanism for SNN that can selectively focus on important nodes and
corresponding features in a graph during the learning process. We evaluate our
proposed method on several benchmark datasets and show that it achieves
comparable performance compared to existing graph learning techniques.

本文提出了一个将注意机制与脉冲神经网络 (SNN) 相结合的新方法，以改进图表示学习的能力。该方法能够在学习过程中选择性地关注图中重要的节点和相应的特征，并在多个基准数据集上进行评估，结果显示其性能与现有的图学习技术相当。

使用注意力驱动的脉冲神经网络增强图表示学习

Enhancing Graph Representation Learning with Attention-Driven Spiking  Neural Networks

Bilevel optimization refers to scenarios whereby the optimal solution of a
lower-level energy function serves as input features to an upper-level
objective of interest. These optimal features typically depend on tunable
parameters of the lower-level energy in such a way that the entire bilevel
pipeline can be trained end-to-end. Although not generally presented as such,
this paper demonstrates how a variety of graph learning techniques can be
recast as special cases of bilevel optimization or simplifications thereof. In
brief, building on prior work we first derive a more flexible class of energy
functions that, when paired with various descent steps (e.g., gradient descent,
proximal methods, momentum, etc.), form graph neural network (GNN)
message-passing layers; critically, we also carefully unpack where any residual
approximation error lies with respect to the underlying constituent
message-passing functions. We then probe several simplifications of this
framework to derive close connections with non-GNN-based graph learning
approaches, including knowledge graph embeddings, various forms of label
propagation, and efficient graph-regularized MLP models. And finally, we
present supporting empirical results that demonstrate the versatility of the
proposed bilevel lens, which we refer to as BloomGML, referencing that BiLevel
Optimization Offers More Graph Machine Learning. Our code is available at
this https URL Let graph ML bloom.

双层优化是指将下层能量函数的最优解作为上层感兴趣的目标的输入特征。本文说明了一系列图学习技术可以被重新解释为双层优化的特殊情况或简化形式，并提出了更灵活的能量函数类，形成图神经网络的消息传递层。此外，本文探索了与非图神经网络图学习方法的密切联系，并通过实证结果展示了所提出的双层优化方法的多样性。

BloomGML: 透过双层优化镜头看图机器学习

BloomGML: Graph Machine Learning through the Lens of Bilevel  Optimization

Audiovisual data is everywhere in this digital age, which raises higher
requirements for the deep learning models developed on them. To well handle the
information of the multi-modal data is the key to a better audiovisual modal.
We observe that these audiovisual data naturally have temporal attributes, such
as the time information for each frame in the video. More concretely, such data
is inherently multi-modal according to both audio and visual cues, which
proceed in a strict chronological order. It indicates that temporal information
is important in multi-modal acoustic event modeling for both intra- and
inter-modal. However, existing methods deal with each modal feature
independently and simply fuse them together, which neglects the mining of
temporal relation and thus leads to sub-optimal performance. With this
motivation, we propose a Temporal Multi-modal graph learning method for
Acoustic event Classification, called TMac, by modeling such temporal
information via graph learning techniques. In particular, we construct a
temporal graph for each acoustic event, dividing its audio data and video data
into multiple segments. Each segment can be considered as a node, and the
temporal relationships between nodes can be considered as timestamps on their
edges. In this case, we can smoothly capture the dynamic information in
intra-modal and inter-modal. Several experiments are conducted to demonstrate
TMac outperforms other SOTA models in performance. Our code is available at
this https URL

我们提出了一种基于时态多模态图学习技术的音频事件分类方法 TMac，通过建模这种时态信息，我们构建了每个音频事件的时态图，通过利用图学习技术来捕捉模态内部和模态间的动态信息，实现了优于其他最先进模型的性能。