Search Results for author: Ji Feng

Found 9 papers, 4 papers with code

Dynamical correlation enhanced orbital magnetization in VI$_{3}$

no code implementations24 Dec 2020 Zhimou Zhou, Shishir Kumar Pandey, Ji Feng

In contrast to the density functional theory that leads to negligible orbital magnetization in VI$_3$, in dynamical mean-field approach the orbital magnetization is greatly enhanced.

Strongly Correlated Electrons

Soft Gradient Boosting Machine

no code implementations7 Jun 2020 Ji Feng, Yi-Xuan Xu, Yuan Jiang, Zhi-Hua Zhou

Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas.

Incremental Learning

Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

1 code implementation NeurIPS 2019 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-targeted) of any corresponding trained classifier during test time when facing clean samples.

GP-RVM: Genetic Programing-based Symbolic Regression Using Relevance Vector Machine

no code implementations7 Jun 2018 Hossein Izadi Rad, Ji Feng, Hitoshi Iba

The solution produced by GP-RVM is a sparse Bayesian linear model of the coefficients of many non-linear functions.

Symbolic Regression

Multi-Layered Gradient Boosting Decision Trees

1 code implementation NeurIPS 2018 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.

Representation Learning

AutoEncoder by Forest

2 code implementations26 Sep 2017 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).

Deep Forest

21 code implementations28 Feb 2017 Zhi-Hua Zhou, Ji Feng

This study opens the door of deep learning based on non-differentiable modules, and exhibits the possibility of constructing deep models without using backpropagation.

Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages

no code implementations7 Dec 2016 Ji Feng, Qingsheng Zhu, Jinlong Huang, Lijun Yang

Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice.

Classification General Classification

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