activity
20192022
most citedRevisiting Over-smoothing in BERT from the Perspective of Graph

7 citations · 9 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20222 cited

Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

Binbin Yang, Xinchi Deng, Han Shi +6

Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual class…

cs.LG20227 cited

Revisiting Over-smoothing in BERT from the Perspective of Graph

Han Shi, Jiahui Gao, Hang Xu +5

Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate th…

cs.LG2021

SparseBERT: Rethinking the Importance Analysis in Self-attention

Han Shi, Jiahui Gao, Xiaozhe Ren +4

Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attent…

cs.LG2019

Effective Decoding in Graph Auto-Encoder using Triadic Closure

Han Shi, Haozheng Fan, James T. Kwok

The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph con…

cs.LG2019

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

Han Shi, Renjie Pi, Hang Xu +3

Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the sea…