no code implementations • 2 Mar 2024 • Xinyi Yu, Ling Yan, PengTao Jiang, Hao Chen, Bo Li, Lin Yuanbo Wu, Linlin Ou
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
no code implementations • 31 Dec 2021 • Xinyi Yu, Ling Yan, Yang Yang, Libo Zhou, Linlin Ou
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Conditional Image Generation Data-free Knowledge Distillation +1
no code implementations • 18 May 2021 • Junhao Hua, Ling Yan, Huan Xu, Cheng Yang
In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization.
no code implementations • 4 Feb 2019 • Kui Zhao, Junhao Hua, Ling Yan, Qi Zhang, Huan Xu, Cheng Yang
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
no code implementations • NeurIPS 2014 • Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang
We theoretically prove that DBH can achieve lower communication cost than existing methods and can simultaneously guarantee good workload balance.