6 citations · 6 across the 2 of their papers we have counts for
3 papers
cs.DC2026
RecIS: Unifying Sparse and Dense Computation for Efficient Recommendation Training
Hua Zong, Shuai Yang, Zhengxiong Zhou +14
Modern recommender models scale sparse representations and dense computation together. They process thousands of heterogeneous feature fields, long user-behavior sequences, and tri…
cs.IR2025
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…
q-fin.GN2022★ 6 cited
Static Replication of Impermanent Loss for Concentrated Liquidity Provision in Decentralised Markets
Jun Deng, Hua Zong, Yun Wang
This article analytically characterizes the impermanent loss of concentrated liquidity provision for automatic market makers in decentralised markets such as Uniswap. We propose tw…