no code implementations • 25 Apr 2024 • Chenyang Wang, Yun Yang
This work introduces a new method for selecting the number of components in finite mixture models (FMMs) using variational Bayes, inspired by the large-sample properties of the Evidence Lower Bound (ELBO) derived from mean-field (MF) variational approximation.
no code implementations • 12 Jan 2024 • Chenyang Wang, Junjun Jiang, Xingyu Hu, Xianming Liu, Xiangyang Ji
Using the measurement, we analyze existing techniques for inverting samples and get some insightful information that inspires a novel loss function to reduce the inconsistency.
1 code implementation • 17 Nov 2023 • Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, Chao Deng
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
no code implementations • 7 Nov 2023 • Baha Zarrouki, Chenyang Wang, Johannes Betz
In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty handling, constraints robustification, feasibility, and closed-loop performance.
no code implementations • 28 Oct 2023 • Baha Zarrouki, Chenyang Wang, Johannes Betz
Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints.
no code implementations • 14 Aug 2023 • Xiao Lin, Xiaokai Chen, Chenyang Wang, Hantao Shu, Linfeng Song, Biao Li, Peng Jiang
To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation.
1 code implementation • 6 Apr 2023 • Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Deming Zhai, Xianming Liu
In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i. e., parsing map) directly from low-resolution face image for the following utilization.
1 code implementation • 4 Mar 2023 • Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
1 code implementation • 4 Jan 2023 • Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
1 code implementation • CVPR 2023 • Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Xianming Liu
To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field.
2 code implementations • 26 Jun 2022 • Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, Shaoping Ma
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
1 code implementation • 25 May 2022 • Chenyang Wang, Junjun Jiang, Xiong Zhou, Xianming Liu
Further, we incorporate our ReSmooth framework with negative data augmentation strategies.
no code implementations • 31 Aug 2021 • Junjun Jiang, Chenyang Wang, Xianming Liu, Kui Jiang, Jiayi Ma
By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (\emph{from less to more}) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.
1 code implementation • ICCV 2021 • Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
1 code implementation • CVPR 2021 • Zongyong Deng, Hao liu, Yaoxing Wang, Chenyang Wang, Zekuan Yu, Xuehong Sun
In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification.
1 code implementation • 11 Jan 2021 • Junjun Jiang, Chenyang Wang, Xianming Liu, Jiayi Ma
Second, we elaborate on the facial characteristics and popular datasets used in FSR.
1 code implementation • ECCV 2020 • Kazuya Nishimura, Junya Hayashida, Chenyang Wang, Dai Fei Elmer Ker, Ryoma Bise
We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of "cell detection" (i. e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining.
1 code implementation • 9 Mar 2019 • Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.
no code implementations • 19 Sep 2018 • Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen
In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.