It is a popular hypothesis in neuroscience that ganglion cells in the retina
are activated by selectively detecting visual features in an observed scene.
While ganglion cell firings can be predicted via data-trained deep neural nets,
the networks remain indecipherable, thus providing little understanding of the
cells' underlying operations. To extract knowledge from the cell firings, in
this paper we learn an interpretable graph-based classifier from data to
predict the firings of ganglion cells in response to visual stimuli.
Specifically, we learn a positive semi-definite (PSD) metric matrix $\mathbf{M}
\succeq 0$ that defines Mahalanobis distances between graph nodes (visual
events) endowed with pre-computed feature vectors; the computed inter-node
distances lead to edge weights and a combinatorial graph that is amenable to
binary classification. Mathematically, we define the objective of metric matrix
$\mathbf{M}$ optimization using a graph adaptation of large margin nearest
neighbor (LMNN), which is rewritten as a semi-definite programming (SDP)
problem. We solve it efficiently via a fast approximation called Gershgorin
disc perfect alignment (GDPA) linearization. The learned metric matrix
$\mathbf{M}$ provides interpretability: important features are identified along
$\mathbf{M}$'s diagonal, and their mutual relationships are inferred from
off-diagonal terms. Our fast metric learning framework can be applied to other
biological systems with pre-chosen features that require interpretation.

从数据中学习可解释的基于图的分类器以预测视觉刺激下的节细胞发射；通过学习正半定 (PSD) 距离矩阵来定义图节点 (视觉事件) 之间的马氏距离，从而导出边权和可进行二元分类的组合图；通过图的半定规划来优化距离矩阵的目标，结合 Gershgorin 圆盘理论和线性化的 Gershgorin 圆盘完美对齐法高效求解；学习到的距离矩阵提供了可解释性，重要特征通过对角线确定并可以从非对角线元素推断出相互关系；该快速度量学习框架适用于其他需要解释的预选特征的生物系统。

视网膜中的信号处理：可解释的图分类器预测神经节细胞响应

Signal Processing in the Retina: Interpretable Graph Classifier to  Predict Ganglion Cell Responses

Dominant sentence ordering models can be classified into pairwise ordering
models and set-to-sequence models. However, there is little attempt to combine
these two types of models, which inituitively possess complementary advantages.
In this paper, we propose a novel sentence ordering framework which introduces
two classifiers to make better use of pairwise orderings for graph-based
sentence ordering. Specially, given an initial sentence-entity graph, we first
introduce a graph-based classifier to predict pairwise orderings between linked
sentences. Then, in an iterative manner, based on the graph updated by
previously predicted high-confident pairwise orderings, another classifier is
used to predict the remaining uncertain pairwise orderings. At last, we adapt a
GRN-based sentence ordering model on the basis of final graph. Experiments on
five commonly-used datasets demonstrate the effectiveness and generality of our
model. Particularly, when equipped with BERT and FHDecoder, our model achieves
state-of-the-art performance.

本文提出了一种新颖的句子排序框架，它引入了两个分类器来更好地利用基于图的句子排序的成对排序，该模型在五个常用数据集上进行了实验，并证明了其有效性和普遍性。