Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) bas...
详细信息
Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accu...
详细信息
The question of what kind of convolutional neural network (CNN) structure performs well is fascinating. In this work, we move toward the answer with one more step by connecting zero stability and model performance. Sp...
详细信息
The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what ...
详细信息
In this paper, we will introduce our work in the 2019 TREC fair ranking task. In temporal academic search, more and more people choose to pay attention to the fairness constraints of ranking. The purpose of this task ...
Visual emotion analysis holds significant research value in both computer vision and psychology. However, existing methods for visual emotion analysis suffer from limited generalizability due to the ambiguity of emoti...
详细信息
Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph re...
详细信息
ISBN:
(数字)9781728160344
ISBN:
(纸本)9781728160351
Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.
With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and r...
With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and redundancy extensively exists in multiple channel ***,overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of *** this paper,channel drop out is proposed to address this problem by data augmentation and ensemble ***,one of all EEG channels will be dropped and replaced by the mean signal of all EEG *** this way,the trial number in the training set was enlarged by channel drop *** at the testing stage,all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the *** experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2 a to verify the performances of the proposed channel drop out *** results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83%and 2.65% compared with the original CNN *** the channel drop out approach significantly improves the performances of motor imagery based BCI.
Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified fo...
Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.
An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph...
详细信息
暂无评论