Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

Download Full-text

Bridgenets: Student-Teacher Transfer Learning Based on Recursive Neural Networks and Its Application to Distant Speech Recognition

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2018.8462137 ◽

2018 ◽

Cited By ~ 4

Author(s):

Jaeyoung Kim ◽

Mostafa El-Khamy ◽

Jungwon Lee

Keyword(s):

Neural Networks ◽

Speech Recognition ◽

Transfer Learning ◽

Student Teacher ◽

Teacher Transfer ◽

Recursive Neural Networks

Download Full-text

Multi-View Attribute Graph Convolution Networks for Clustering

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/411 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiafeng Cheng ◽

Qianqian Wang ◽

Zhiqiang Tao ◽

Deyan Xie ◽

Quanxue Gao

Keyword(s):

Neural Networks ◽

State Of The Art ◽

Graph Embedding ◽

Structured Data ◽

Attention Networks ◽

Graph Data ◽

Graph Reconstruction ◽

Node Attributes ◽

Graph Neural Networks ◽

Geometric Relationship

Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods can not allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly focus on the case of multiple graphs, while designing GNNs for solving graph-structured data of multi-view attributes is still under-explored. In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task. MAGCN is designed with two-pathway encoders that map graph embedding features and learn the view-consistency information. Specifically, the first pathway develops multi-view attribute graph attention networks to reduce the noise/redundancy and learn the graph embedding features for each multi-view graph data. The second pathway develops consistent embedding encoders to capture the geometric relationship and probability distribution consistency among different views, which adaptively finds a consistent clustering embedding space for multi-view attributes. Experiments on three benchmark graph datasets show the superiority of our method compared with several state-of-the-art algorithms.

Download Full-text