Adaptive machine learning (ML) aims to allow ML models to adapt to
ever-changing environments with potential concept drift after model deployment.
Traditionally, adaptive ML requires a new dataset to be manually labeled to
tailor deployed models to altered data distributions. Recently, an interactive
causality based self-labeling method was proposed to autonomously associate
causally related data streams for domain adaptation, showing promising results
compared to traditional feature similarity-based semi-supervised learning.
Several unanswered research questions remain, including self-labeling's
compatibility with multivariate causality and the quantitative analysis of the
auxiliary models used in the self-labeling. The auxiliary models, the
interaction time model (ITM) and the effect state detector (ESD), are vital to
the success of self-labeling. This paper further develops the self-labeling
framework and its theoretical foundations to address these research questions.
A framework for the application of self-labeling to multivariate causal graphs
is proposed using four basic causal relationships, and the impact of non-ideal
ITM and ESD performance is analyzed. A simulated experiment is conducted based
on a multivariate causal graph, validating the proposed theory.

自适应机器学习应用于变化环境下的模型自适应，通过实现与因果相关的数据流自我标记方法，结合多变量因果关系和辅助模型的应用，提出了针对多变量因果图的自标记应用框架及评估方法。

自标注的多变量因果关系与自适应机器学习的量化

Self-Labeling in Multivariate Causality and Quantification for Adaptive  Machine Learning

We design and implement an adaptive machine learning equalizer that
alternates multiple linear and nonlinear computational layers on an FPGA.
On-chip training via gradient backpropagation is shown to allow for real-time
adaptation to time-varying channel impairments.

本文设计和实现了一种自适应机器学习均衡器，通过 FPGA 交替多个线性和非线性计算层，并采用梯度反向传播实现芯片内训练，以实现对时变信道受损的实时自适应。

基于 FPGA 的多层机器学习均衡器及芯片内训练实现

FPGA Implementation of Multi-Layer Machine Learning Equalizer with  On-Chip Training

Neural machine learning methods, such as deep neural networks (DNN), have
achieved remarkable success in a number of complex data processing tasks. These
methods have arguably had their strongest impact on tasks such as image and
audio processing - data processing domains in which humans have long held clear
advantages over conventional algorithms. In contrast to biological neural
systems, which are capable of learning continuously, deep artificial networks
have a limited ability for incorporating new information in an already trained
network. As a result, methods for continuous learning are potentially highly
impactful in enabling the application of deep networks to dynamic data sets.
Here, inspired by the process of adult neurogenesis in the hippocampus, we
explore the potential for adding new neurons to deep layers of artificial
neural networks in order to facilitate their acquisition of novel information
while preserving previously trained data representations. Our results on the
MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes
lower and upper case letters and digits, demonstrate that neurogenesis is well
suited for addressing the stability-plasticity dilemma that has long challenged
adaptive machine learning algorithms.

本文通过加入新神经元到深度神经网络的层中，探索了连续学习的潜力，并通过 MNIST 手写数字数据集和 NIST SD 19 数据集证明了神经发生有助于解决适应性机器学习算法中的稳定性和可塑性两难问题。