no code implementations • ICML 2020 • Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
no code implementations • 29 Sep 2023 • Cheng Zheng, Guangyuan Zhao, Peter T. C. So
To bridge this gap, we, for the first time, propose a fully differentiable design framework that integrates a pre-trained photolithography simulator into the model-based optical design loop.
no code implementations • 28 Aug 2023 • Juan Leon-Alcazar, Yazeed Alnumay, Cheng Zheng, Hassane Trigui, Sahejad Patel, Bernard Ghanem
We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading.
no code implementations • 19 Oct 2022 • Navodini Wijethilake, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So, Murat Yildirim, Dushan N. Wadduwage
Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy.
no code implementations • 23 Mar 2022 • Bing Li, Cheng Zheng, Guohao Li, Bernard Ghanem
To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks.
no code implementations • 10 Oct 2021 • Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zheng, Miltiadis Allamanis
Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges).
1 code implementation • 10 May 2021 • Bing Li, Cheng Zheng, Silvio Giancola, Bernard Ghanem
We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds.
no code implementations • ICCV 2021 • Bing Li, Chia-Wen Lin, Cheng Zheng, Shan Liu, Junsong Yuan, Bernard Ghanem, C.-C. Jay Kuo
In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure.
Vocal Bursts Intensity Prediction Vocal Bursts Valence Prediction
1 code implementation • 13 Nov 2019 • Lili Wang, Xiaodong Luo, Cheng Zheng
The integration and application of maxcombo tests in interim analyses often require extensive simulation studies.
Methodology
1 code implementation • 27 Sep 2018 • Ying Liu, Cheng Zheng
A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR.
Methodology
1 code implementation • ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 • Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.