A recent paper by Boughammoura (2023) describes the back-propagation
algorithm in terms of an alternative formulation called the F-adjoint method.
In particular, by the F-adjoint algorithm the computation of the loss gradient,
with respect to each weight within the network, is straightforward and can
simply be done. In this work, we develop and investigate this theoretical
framework to improve some supervised learning algorithm for feed-forward neural
network. Our main result is that by introducing some neural dynamical model
combined by the gradient descent algorithm, we derived an equilibrium F-adjoint
process which yields to some local learning rule for deep feed-forward networks
setting. Experimental results on MNIST and Fashion-MNIST datasets, demonstrate
that the proposed approach provide a significant improvements on the standard
back-propagation training procedure.

该研究论文介绍了一种名为 F-adjoint 方法的替代性反向传播算法，通过引入神经动力模型与梯度下降算法相结合，提出了一种局部学习规则以改进前馈神经网络的监督学习算法，并通过在 MNIST 和 Fashion-MNIST 数据集上的实验证明了所提出方法对标准反向传播训练过程的显著改进。

F - 共轭学习

Learning by the F-adjoint

This paper targets the problem of speech act detection in conversations about
bug repair. We conduct a "Wizard of Oz" experiment with 30 professional
programmers, in which the programmers fix bugs for two hours, and use a
simulated virtual assistant for help. Then, we use an open coding manual
annotation procedure to identify the speech act types in the conversations.
Finally, we train and evaluate a supervised learning algorithm to automatically
detect the speech act types in the conversations. In 30 two-hour conversations,
we made 2459 annotations and uncovered 26 speech act types. Our automated
detection achieved 69% precision and 50% recall. The key application of this
work is to advance the state of the art for virtual assistants in software
engineering. Virtual assistant technology is growing rapidly, though
applications in software engineering are behind those in other areas, largely
due to a lack of relevant data and experiments. This paper targets this problem
in the area of developer Q/A conversations about bug repair.

基于 “巫师奥兹” 的实验，利用自然语言处理标注工具自动检测软件工程中开发者关于漏洞修复方面的交流中的语音行为类型，可用于提高虚拟助手在软件工程中的自动交流能力。

在程序员问答对话中检测语言行为类型：修复错误期间

Detecting Speech Act Types in Developer Question/Answer Conversations  During Bug Repair

This article presents Individual Conditional Expectation (ICE) plots, a tool
for visualizing the model estimated by any supervised learning algorithm.
Classical partial dependence plots (PDPs) help visualize the average partial
relationship between the predicted response and one or more features. In the
presence of substantial interaction effects, the partial response relationship
can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate
the complexity of the modeled relationship. Accordingly, ICE plots refine the
partial dependence plot by graphing the functional relationship between the
predicted response and the feature for individual observations. Specifically,
ICE plots highlight the variation in the fitted values across the range of a
covariate, suggesting where and to what extent heterogeneities might exist. In
addition to providing a plotting suite for exploratory analysis, we include a
visual test for additive structure in the data generating model. Through
simulated examples and real data sets, we demonstrate how ICE plots can shed
light on estimated models in ways PDPs cannot. Procedures outlined are
available in the R package ICEbox.

本文提出了个体条件期望（ICE）图形，这是一种可视化任何监督学习算法估计模型的工具，通过绘制每个观测值在特征的范围上的预测响应和特征之间的函数关系，突出了拟合值在协变量范围内的变化，提供了探索性分析的绘图工具和数据生成模型的可加结构的可视化检验。