Jingfei Chang

Orcid: 0000-0003-0530-6511

According to our database1, Jingfei Chang authored at least 19 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Efficient multi-objective neural architecture search framework via policy gradient algorithm.
Inf. Sci., 2024

Deep Prompt Multi-task Network for Abuse Language Detection.
CoRR, 2024

2023
IR<sup>2</sup>Net: information restriction and information recovery for accurate binary neural networks.
Neural Comput. Appl., July, 2023

Iterative clustering pruning for convolutional neural networks.
Knowl. Based Syst., April, 2023

CLIP Multi-modal Hashing: A new baseline CLIPMH.
CoRR, 2023

Fast and Accurate Binary Neural Networks Based on Depth-Width Reshaping.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Global balanced iterative pruning for efficient convolutional neural networks.
Neural Comput. Appl., 2022

IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks.
CoRR, 2022

Self-distribution binary neural networks.
Appl. Intell., 2022

Automatic channel pruning via clustering and swarm intelligence optimization for CNN.
Appl. Intell., 2022

2021
MPT-embedding: An unsupervised representation learning of code for software defect prediction.
J. Softw. Evol. Process., 2021

Compressing convolutional neural networks via intermediate features.
J. Intell. Fuzzy Syst., 2021

AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks.
CoRR, 2021

ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN.
CoRR, 2021

2020
PathPair2Vec: An AST path pair-based code representation method for defect prediction.
J. Comput. Lang., 2020

Coarse and fine-grained automatic cropping deep convolutional neural network.
CoRR, 2020

UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks Compression and Acceleration.
CoRR, 2020

2019
Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance.
IEEE Access, 2019

Mapping Bug Reports to Relevant Source Code Files Based on the Vector Space Model and Word Embedding.
IEEE Access, 2019


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