Search Results for author: Huaming Du

Found 5 papers, 2 papers with code

Taming Gradient Variance in Federated Learning with Networked Control Variates

no code implementations26 Oct 2023 Xingyan Chen, Yaling Liu, Huaming Du, Mu Wang, Yu Zhao

To address this, we introduce a novel Networked Control Variates (FedNCV) framework for Federated Learning.

Federated Learning

A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective

no code implementations28 Nov 2022 Yu Zhao, Huaming Du, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).

Management

ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling

no code implementations27 Nov 2022 Yu Guo, Zhilong Xie, Xingyan Chen, Huangen Chen, Leilei Wang, Huaming Du, Shaopeng Wei, Yu Zhao, Qing Li, Gang Wu

We address the problem by introducing a novel joint method on top of BERT which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby contributing to the two tasks.

intent-classification Intent Classification +5

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

1 code implementation11 Jan 2022 Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.

Implicit Relations Stock Prediction

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks

1 code implementation24 Dec 2021 Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.

Graph Learning

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