1.2k citations · 1.9k across the 34 of their papers we have counts for
8 papers · 1 filter
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
Jiyang Xie, Zhanyu Ma, Jing-Hao Xue +2
This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based uncertainty inference (UI) in deep neural network (DNN)-based…
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Jiyang Xie, Zhanyu Ma, and Jianjun Lei +4
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate o…
A Concise Review of Recent Few-shot Meta-learning Methods
Xiaoxu Li, Zhuo Sun, Jing-Hao Xue +1
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give…
Discriminant analysis based on projection onto generalized difference subspace
Kazuhiro Fukui, Naoya Sogi, Takumi Kobayashi +2
This paper discusses a new type of discriminant analysis based on the orthogonal projection of data onto a generalized difference subspace (GDS). In our previous work, we have demo…
SEA: A Combined Model for Heat Demand Prediction
Jiyang Xie, Jiaxin Guo, Zhanyu Ma +4
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristic…
BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
Jiyang Xie, Zhanyu Ma, Guoqiang Zhang +4
A Bayesian approach termed BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaus…
Metric Learning via Maximizing the Lipschitz Margin Ratio
Mingzhi Dong, Xiaochen Yang, Yang Wu +1
In this paper, we propose the Lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio. This framework enables the integration of…
Learning Local Metrics and Influential Regions for Classification
Mingzhi Dong, Yujiang Wang, Xiaochen Yang +1
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data. To address the problem…