Today, state-of-the-art deep neural networks that process events first
convert them into dense, grid-like input representations before using an
off-the-shelf network. However, selecting the appropriate representation for
the task traditionally requires training a neural network for each
representation and selecting the best one based on the validation score, which
is very time-consuming. In this work, we eliminate this bottleneck by selecting
the best representation based on the Gromov-Wasserstein Discrepancy (GWD)
between the raw events and their representation. It is approximately 200 times
faster to compute than training a neural network and preserves the task
performance ranking of event representations across multiple representations,
network backbones, and datasets. This means that finding a representation with
a high task score is equivalent to finding a representation with a low GWD. We
use this insight to, for the first time, perform a hyperparameter search on a
large family of event representations, revealing new and powerful
representations that exceed the state-of-the-art. On object detection, our
optimized representation outperforms existing representations by 1.9% mAP on
the 1 Mpx dataset and 8.6% mAP on the Gen1 dataset and even outperforms the
state-of-the-art by 1.8% mAP on Gen1 and state-of-the-art feed-forward methods
by 6.0% mAP on the 1 Mpx dataset. This work opens a new unexplored field of
explicit representation optimization for event-based learning methods.

本文提出了一种基于 Gromov-Wasserstein 差异的事件表示优化方法，可以更快地找到最佳表示方法，优化后的表示方法在物体检测中的表现超过了现有的最先进表示方法，为事件学习方法的显式表示优化开辟了新的研究领域。

混沌中求序：为物体检测排序事件表征

From Chaos Comes Order: Ordering Event Representations for Object  Detection

We propose a new nonlinear factorization model for graphs that are with
topological structures, and optionally, node attributes. This model is based on
a pseudometric called Gromov-Wasserstein (GW) discrepancy, which compares
graphs in a relational way. It estimates observed graphs as GW barycenters
constructed by a set of atoms with different weights. By minimizing the GW
discrepancy between each observed graph and its GW barycenter-based estimation,
we learn the atoms and their weights associated with the observed graphs. The
model achieves a novel and flexible factorization mechanism under GW
discrepancy, in which both the observed graphs and the learnable atoms can be
unaligned and with different sizes. We design an effective approximate
algorithm for learning this Gromov-Wasserstein factorization (GWF) model,
unrolling loopy computations as stacked modules and computing gradients with
backpropagation. The stacked modules can be with two different architectures,
which correspond to the proximal point algorithm (PPA) and Bregman alternating
direction method of multipliers (BADMM), respectively. Experiments show that
our model obtains encouraging results on clustering graphs.

本文提出了一种基于 Gromov-Wasserstein 距离的新型非线性因子分解模型，可用于关系图的聚类。

基于 Gromov-Wasserstein 分解的图聚类模型

Gromov-Wasserstein Factorization Models for Graph Clustering

A novel Gromov-Wasserstein learning framework is proposed to jointly match
(align) graphs and learn embedding vectors for the associated graph nodes.
Using Gromov-Wasserstein discrepancy, we measure the dissimilarity between two
graphs and find their correspondence, according to the learned optimal
transport. The node embeddings associated with the two graphs are learned under
the guidance of the optimal transport, the distance of which not only reflects
the topological structure of each graph but also yields the correspondence
across the graphs. These two learning steps are mutually-beneficial, and are
unified here by minimizing the Gromov-Wasserstein discrepancy with structural
regularizers. This framework leads to an optimization problem that is solved by
a proximal point method. We apply the proposed method to matching problems in
real-world networks, and demonstrate its superior performance compared to
alternative approaches.

该论文提出一种基于 Gromov-Wasserstein 方法的图对齐及嵌入学习框架，通过学习最优传输距离完成图的对齐，以及标记距离来引导嵌入向量的学习，并通过结构正则化减小 Gromov-Wasserstein 距离，使用近端点法求解这个优化问题。该方法在现实网络中的匹配问题上具有卓越的性能表现。