activity
20182023
most citedTowards Understanding Mixture of Experts in Deep Learning

23 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023★ 2 cited

Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions

Weizhen He, Yiheng Deng, Shixiang Tang +9

Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identificat…

cs.LG2023★ 1 cited

Robust Learning with Progressive Data Expansion Against Spurious Correlation

Yihe Deng, Yu Yang, Baharan Mirzasoleiman +1

While deep learning models have shown remarkable performance in various tasks, they are susceptible to learning non-generalizable spurious features rather than the core features th…

cs.LG2022★ 23 cited

Towards Understanding Mixture of Experts in Deep Learning

Zixiang Chen, Yihe Deng, Yue Wu +2

The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlled by a router, has achieved great success in deep learning. However, the understanding of such architecture…

cs.CL2020★ 4 cited

Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks

Xiaosen Wang, Yichen Yang, Yihe Deng +1

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification.For text classification, however, exis…

cs.DB2018

Adaptive Wavelet Clustering for Highly Noisy Data

Zengjian Chen, Jiayi Liu, Yihe Deng +2

In this paper we make progress on the unsupervised task of mining arbitrarily shaped clusters in highly noisy datasets, which is a task present in many real-world applications. Bas…