most citedHolistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

51 citations

6 papers

cs.CL2023★ 5 cited

A Concept Knowledge Graph for User Next Intent Prediction at Alipay

Yacheng He, Qianghuai Jia, Lin Yuan +3

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 mill…

cs.LG2022★ 18 cited

Lexicographic Multi-Objective Reinforcement Learning

Joar Skalse, Lewis Hammond, Charlie Griffin +1

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where t…

cs.LG2022★ 10 cited

End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Shiyu Wang, Fan Zhou, Yinbo Sun +3

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconc…

cs.CV2022★ 23 cited

NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

Bin Tan, Nan Xue, Tianfu Wu +1

This paper studies the challenging two-view 3D reconstruction in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs…

cs.CV2022★ 51 cited

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

Nan Xue, Tianfu Wu, Song Bai +4

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAW…

cs.CR2022★ 30 cited

A Customized Text Sanitization Mechanism with Differential Privacy

Huimin Chen, Fengran Mo, Yanhao Wang +4

As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differ…