Several applications, analysis and visualization of signal demand representation of time-
domain signal in different domains like frequency-domain representation based on Fourier …

We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior
on standard models of random graphs, where nodes are represented by random latent …

Objective: Fluctuations in heart rate are intimately related to changes in the physiological
state of the organism. We exploit this relationship by classifying a human participant's …

Continual learning has been widely studied in recent years to resolve the catastrophic
forgetting of deep neural networks. In this paper, we first enforce a low-rank filter subspace …

Image understanding has long been a goal for computer vision. It has proved to be an
exceptionally difficult task due to the large amounts of variability that are inherent to objects …

随着训练可用数据量的增长与计算平台处理能力的增强, 基于深度学习的智能模型能够完成越来
越复杂的任务, 其在计算机视觉, 自然语言处理等人工智能领域已经取得重大的突破. 然而 …

Analyzing representational similarity among neural networks (NNs) is essential for
interpreting or transferring deep models. In application scenarios where numerous NN …

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-
Shot Learning (FSL) problem across different domains has attracted rising attention. The …

Numerical experiments demonstrate that deep neural network classifiers progressively
separate class distributions around their mean, achieving linear separability on the training …

The encoder-decoder network (ED-Net) is a commonly employed choice for existing depth
completion methods but its working mechanism is ambiguous. In this paper we visualize the …