32 citations · 68 across the 7 of their papers we have counts for
7 papers
Deep Clustering Survival Machines with Interpretable Expert Distributions
Bojian Hou, Hongming Li, Zhicheng Jiao +3
Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. I…
Online Deep Learning from Doubly-Streaming Data
Heng Lian, John Scovil Atwood, Bojian Hou +2
This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features e…
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong +5
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic functi…
Storage Fit Learning with Feature Evolvable Streams
Bo-Jian Hou, Yu-Hu Yan, Peng Zhao +1
Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usua…
Prediction with Unpredictable Feature Evolution
Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge. Its goal is to keep the m…
Learning with Interpretable Structure from Gated RNN
Bo-Jian Hou, Zhi-Hua Zhou
The interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models.…
Learning with Feature Evolvable Streams
Bo-Jian Hou, Lijun Zhang, Zhi-Hua Zhou
Learning with streaming data has attracted much attention during the past few years. Though most studies consider data stream with fixed features, in real practice the features may…