88 citations · 146 across the 5 of their papers we have counts for
6 papers · 1 filter
Soft Gradient Boosting Machine
Ji Feng, Yi-Xuan Xu, Yuan Jiang +1
Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base l…
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
Ji Feng, Qi-Zhi Cai, Zhi-Hua Zhou
In this work, we consider one challenging training time attack by modifying training data with bounded perturbation, hoping to manipulate the behavior (both targeted or non-targete…
Multi-Layered Gradient Boosting Decision Trees
Ji Feng, Yang Yu, Zhi-Hua Zhou
Multi-layered representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such a…
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
Ya-Lin Zhang, Jun Zhou, Wenhao Zheng +10
Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which c…
AutoEncoder by Forest
Ji Feng, Zhi-Hua Zhou
Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (…
Deep Forest
Zhi-Hua Zhou, Ji Feng
Current deep learning models are mostly build upon neural networks, i.e., multiple layers of parameterized differentiable nonlinear modules that can be trained by backpropagation.…