activity
20202022
most citedCode Smells in Machine Learning Systems

12 citations · 28 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CL2022

Ask to Understand: Question Generation for Multi-hop Question Answering

Jiawei Li, Mucheng Ren, Yang Gao +1

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Questio…

cs.SE202212 cited

Code Smells in Machine Learning Systems

Jiri Gesi, Siqi Liu, Jiawei Li +6

As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more compl…

cs.LG20219 cited

MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps

Muhammad Awais, Fengwei Zhou, Chuanlong Xie +3

Deep neural networks are susceptible to adversarially crafted, small and imperceptible changes in the natural inputs. The most effective defense mechanism against these examples is…

cs.SE20213 cited

PyNose: A Test Smell Detector For Python

Tongjie Wang, Yaroslav Golubev, Oleg Smirnov +3

Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the pr…

cs.CV20213 cited

Salient Object Ranking with Position-Preserved Attention

Hao Fang, Daoxin Zhang, Yi Zhang +5

Instance segmentation can detect where the objects are in an image, but hard to understand the relationship between them. We pay attention to a typical relationship, relative salie…

cs.LG2021

Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework

Yuan Gao, Jiawei Li, Maoguo Gong +2

Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approache…