405 citations · 2.3k across the 116 of their papers we have counts for
11 papers · 1 filter
Green Learning: Introduction, Examples and Outlook
C. -C. Jay Kuo, Azad M. Madni
Rapid advances in artificial intelligence (AI) in the last decade have largely been built upon the wide applications of deep learning (DL). However, the high carbon footprint yield…
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
Hongyu Fu, Yijing Yang, Yuhuai Liu +4
Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classifi…
Label Efficient Regularization and Propagation for Graph Node Classification
Tian Xie, Rajgopal Kannan, C. -C. Jay Kuo
An enhanced label propagation (LP) method called GraphHop was proposed recently. It outperforms graph convolutional networks (GCNs) in the semi-supervised node classification task…
CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding
Xiou Ge, Yun-Cheng Wang, Bin Wang +1
Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is pro…
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
Yun-Cheng Wang, Xiou Ge, Bin Wang +1
Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in kno…
Constructing Multilayer Perceptrons as Piecewise Low-Order Polynomial Approximators: A Signal Processing Approach
Ruiyuan Lin, Suya You, Raghuveer Rao +1
The construction of a multilayer perceptron (MLP) as a piecewise low-order polynomial approximator using a signal processing approach is presented in this work. The constructed MLP…
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
Yueru Chen, C. -C. Jay Kuo
A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work. SSL contains four key ingredients: 1) successive near-to-far neighborhood…
Graph Representation Learning: A Survey
Fenxiao Chen, Yuncheng Wang, Bin Wang +1
Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph…
Convolutional Neural Networks with Transformed Input based on Robust Tensor Network Decomposition
Jenn-Bing Ong, Wee-Keong Ng, C. -C. Jay Kuo
Tensor network decomposition, originated from quantum physics to model entangled many-particle quantum systems, turns out to be a promising mathematical technique to efficiently re…
Analysis on Gradient Propagation in Batch Normalized Residual Networks
Abhishek Panigrahi, Yueru Chen, C. -C. Jay Kuo
We conduct mathematical analysis on the effect of batch normalization (BN) on gradient backpropogation in residual network training, which is believed to play a critical role in ad…
On Extended Long Short-term Memory and Dependent Bidirectional Recurrent Neural Network
Yuanhang Su, C. -C. Jay Kuo
In this work, we first analyze the memory behavior in three recurrent neural networks (RNN) cells; namely, the simple RNN (SRN), the long short-term memory (LSTM) and the gated rec…