activity
20182022
most citedUnderstanding and Improving Layer Normalization

178 citations · 345 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG202147 cited

Topology-Imbalance Learning for Semi-Supervised Node Classification

Deli Chen, Yankai Lin, Guangxiang Zhao +4

The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existin…

cs.CL20211 cited

Learning Relation Alignment for Calibrated Cross-modal Retrieval

Shuhuai Ren, Junyang Lin, Guangxiang Zhao +5

Despite the achievements of large-scale multimodal pre-training approaches, cross-modal retrieval, e.g., image-text retrieval, remains a challenging task. To bridge the semantic ga…

cs.CL201977 cited

Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang +3

Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self-attention is able to model long-term depe…

cs.CL201942 cited

MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning

Guangxiang Zhao, Xu Sun, Jingjing Xu +2

In sequence to sequence learning, the self-attention mechanism proves to be highly effective, and achieves significant improvements in many tasks. However, the self-attention mecha…

cs.LG2019178 cited

Understanding and Improving Layer Normalization

Jingjing Xu, Xu Sun, Zhiyuan Zhang +2

Layer normalization (LayerNorm) is a technique to normalize the distributions of intermediate layers. It enables smoother gradients, faster training, and better generalization accu…

cs.CL2018

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

Guangxiang Zhao, Jingjing Xu, Qi Zeng +1

This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music…