Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation

时间片段洗牌（TPS）：利用片段级洗牌提升时间序列预测的泛化能力和鲁棒性

Sliding-Window Reordering with Overlap Averaging: A Simple Time-Domain Augmentation for Multivariate Forecasting

本文提出TPS，一种简单且模型无关的数据增强方法，通过提取重叠时间片段并基于方差排序选择性洗牌，提升时间序列预测的泛化能力和鲁棒性。

采用滑动排列不变训练的可变数量声源位置跟踪

Position tracking of a varying number of sound sources with sliding permutation invariant training

针对声源不断出现和消失的多源跟踪问题，提出基于先前帧最优位置关联均方误差的滑动排列不变训练策略，可在不降低逐帧定位精度的情况下减少身份切换。

Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations

Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. While performances reach expert's level, interpretability (highlight how and what a trained model learned and why it makes a specific decision) is the next important challenge that deep learning methods need to answer to be fully integrated in the medical field. In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. We formalize the design of WSI classification architectures and propose a piece-wise interpretability app…

一种可调的机器学习框架用于分步合成规划

A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning

MHNpath是一种可调机器学习框架，用于分步合成规划，通过优化反应路径优先级，提高合成预测的准确性和环保性。

Improving Reliability of Fine-tuning with Block-wise Optimisation

Finetuning can be used to tackle domain-specific tasks by transferring knowledge. Previous studies on finetuning focused on adapting only the weights of a task-specific classifier or re-optimizing all layers of the pre-trained model using the new task data. The first type of methods cannot mitigate the mismatch between a pre-trained model and the new task data, and the second type of methods easily cause over-fitting when processing tasks with limited data. To explore the effectiveness of fine-tuning, we propose a novel block-wise optimization mechanism, which adapts the weights of a group of …

Piece-wise Matching Layer in Representation Learning for ECG Classification

This paper proposes piece-wise matching layer as a novel layer in representation learning methods for electrocardiogram (ECG) classification. Despite the remarkable performance of representation learning methods in the analysis of time series, there are still several challenges associated with these methods ranging from the complex structures of methods, the lack of generality of solutions, the need for expert knowledge, and large-scale training datasets. We introduce the piece-wise matching layer that works based on two levels to address some of the aforementioned challenges. At the first lev…