3 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 3 cited
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer
Boan Liu, Liang Ding, Li Shen +4
The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning, based on the principle of divide-and-conquer to maximize model capacity without signific…
cs.LG2022★ 3 cited
PAD-Net: An Efficient Framework for Dynamic Networks
Shwai He, Liang Ding, Daize Dong +3
Dynamic networks, e.g., Dynamic Convolution (DY-Conv) and the Mixture of Experts (MoE), have been extensively explored as they can considerably improve the model's representation p…
cs.CL2022★ 3 cited
Vega-MT: The JD Explore Academy Translation System for WMT22
Changtong Zan, Keqin Peng, Liang Ding +9
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task. We participated in all high-resource tracks and one medium-resource track, includin…