most citedBFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SI2023

Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation

Jin-Duk Park, Siqing Li, Xin Cao +1

The multi-criteria (MC) recommender system, which leverages MC rating information in a wide range of e-commerce areas, is ubiquitous nowadays. Surprisingly, although graph neural n…

cs.CL2023

Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling

Linhan Zhang, Qian Chen, Wen Wang +4

Definition modeling is an important task in advanced natural language applications such as understanding and conversation. Since its introduction, it focus on generating one defini…

cs.SI2022

Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation

Jin-Duk Park, Cong Tran, Won-Yong Shin +1

Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, the…

cs.LG20221 cited

BFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization

Xin Cao

In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed. This approach can be applied to design non-adaptive learning rat…

cs.LG2022

Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization

Xin Cao

A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE). The Adaptive…