activity
20152022
most citedClass-Balanced Loss Based on Effective Number of Samples

130 citations · 289 across the 10 of their papers we have counts for

collaborators

18 papers

cs.SE202228 cited

Toward Interactive Bug Reporting for (Android App) End-Users

Yang Song, Junayed Mahmud, Ying Zhou +4

Many software bugs are reported manually, particularly bugs that manifest themselves visually in the user interface. End-users typically report these bugs via app reviewing website…

cs.CV2022

Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond

Tiange Xiang, Chaoyi Zhang, Xinyi Wang +4

U-Net, as an encoder-decoder architecture with forward skip connections, has achieved promising results in various medical image analysis tasks. Many recent approaches have also ex…

cs.CL2019

Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

Junyi Li, Wayne Xin Zhao, Ji-Rong Wen +1

Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical…

cs.CV2019

Geo-Aware Networks for Fine-Grained Recognition

Grace Chu, Brian Potetz, Weijun Wang +5

Fine-grained recognition distinguishes among categories with subtle visual differences. In order to differentiate between these challenging visual categories, it is helpful to leve…

cs.CV2019130 cited

Class-Balanced Loss Based on Effective Number of Samples

Yin Cui, Menglin Jia, Tsung-Yi Lin +2

With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of long-tailed data distribution (i.e., a few classes account for most of th…

cs.LG2018

Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning

Zhibo Wang, Mengkai Song, Zhifei Zhang +3

Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentral…