Physiological signal analysis often involves identifying events crucial to
understanding biological dynamics. Traditional methods rely on handcrafted
procedures or supervised learning, presenting challenges such as expert
dependence, lack of robustness, and the need for extensive labeled data.
Data-driven methods like Convolutional Dictionary Learning (CDL) offer an
alternative but tend to produce spurious detections. This work introduces UNHaP
(Unmix Noise from Hawkes Processes), a novel approach addressing the joint
learning of temporal structures in events and the removal of spurious
detections. Leveraging marked Hawkes processes, UNHaP distinguishes between
events of interest and spurious ones. By treating the event detection output as
a mixture of structured and unstructured events, UNHaP efficiently unmixes
these processes and estimates their parameters. This approach significantly
enhances the understanding of event distributions while minimizing false
detection rates.

使用 UNHaP（Unmix Noise from Hawkes Processes）方法，本研究介绍了一种新的方法来处理生理信号分析中的事件检测和噪音问题，通过联合学习事件的时间结构和去除虚假检测，该方法有效地减少了假阳性检测率。

从 Hawkes 过程中去除噪声以建模学习到的生理事件

Unmixing Noise from Hawkes Process to Model Learned Physiological Events

While a number of different algorithms have recently been proposed for
convolutional dictionary learning, this remains an expensive problem. The
single biggest impediment to learning from large training sets is the memory
requirements, which grow at least linearly with the size of the training set
since all existing methods are batch algorithms. The work reported here
addresses this limitation by extending online dictionary learning ideas to the
convolutional context.

本文研究了针对卷积字典学习的在线学习方法，解决了传统批量算法在处理大规模训练数据时所需的较高内存要求的问题。

在线卷积字典学习

Online Convolutional Dictionary Learning

A generative model is developed for deep (multi-layered) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up (pretraining) and top-down
(refinement) probabilistic learning. Experimental results demonstrate powerful
capabilities of the model to learn multi-layer features from images, and
excellent classification results are obtained on the MNIST and Caltech 101
datasets.

本文开发了一种生成模型，其中将新颖的概率池化操作集成到深度模型中，从而产生有效的自下而上（预训练）和自上而下（精细化）概率学习。实验结果表明该模型具有学习图像多层特征的强大能力，并在 MNIST 和 Caltech 101 数据集上获得了优秀的分类结果。