Jialing Zhang

Orcid: 0000-0001-5484-3511

According to our database1, Jialing Zhang authored at least 30 papers between 2017 and 2024.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Biobjective optimization for railway alignment fine-grained designs with parallel existing railways.
Comput. Aided Civ. Infrastructure Eng., February, 2024

Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data.
CoRR, 2024

Blow-up at infinity of solutions to a class of pseudo-parabolic equations with logarithmic nonlinearity and singular potential.
Appl. Math. Lett., 2024

2023
Normal mapping and normal transfer for geometric dynamic models.
Multim. Tools Appl., 2023

Back to the Source: Diffusion-Driven Adaptation to Test-Time Corruption.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
One-to-many comparative summarization for patents.
Scientometrics, 2022

Tighter monogamy relations in multi-qubit systems.
Quantum Inf. Process., 2022

Towards better time series prediction with model-independent, low-dispersion clusters of contextual subsequence embeddings.
Knowl. Based Syst., 2022

Back to the Source: Diffusion-Driven Test-Time Adaptation.
CoRR, 2022

2021
A Novel Transfer Dictionary Learning Strategy for Rolling Bearing Fault Identification With a Mixed Noise Model.
IEEE Trans. Instrum. Meas., 2021

Extracting depressive symptoms and their associations from an online depression community.
Comput. Hum. Behav., 2021

DPZ: Improving Lossy Compression Ratio with Information Retrieval on Scientific Data.
Proceedings of the IEEE International Conference on Cluster Computing, 2021

Cascaded Dimension Reduction for Effective Anomaly Detection.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Loss Attenuation for Time Series Prediction Respecting Categories of Values.
Proceedings of the Web and Big Data - 5th International Joint Conference, 2021

2020
Dictionary Learning via a Mixed Noise Model for Sparse Representation Classification of Rolling Bearings.
IEEE Access, 2020

Bit-Error Aware Quantization for DCT-based Lossy Compression.
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020

Anomaly Detection in Edge Nodes using Sparsity Profile.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Correction to: MISC: missing imputation for single-cell RNA sequencing data.
BMC Syst. Biol., 2019

Efficient Encoding and Reconstruction of HPC Datasets for Checkpoint/Restart.
Proceedings of the 35th Symposium on Mass Storage Systems and Technologies, 2019

Towards Improving Rate-Distortion Performance of Transform-Based Lossy Compression for HPC Datasets.
Proceedings of the 2019 IEEE High Performance Extreme Computing Conference, 2019

AD<sup>2</sup>: Improving Quality of IoT Data through Compressive Anomaly Detection.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
A measure-driven method for normal mapping and normal map design of 3D models.
Multim. Tools Appl., 2018

A 3D face registration algorithm based on conformal mapping.
Concurr. Comput. Pract. Exp., 2018

Evaluating fidelity of lossy compression on spatiotemporal data from an IoT enabled smart farm.
Comput. Electron. Agric., 2018

MISC: missing imputation for single-cell RNA sequencing data.
BMC Syst. Biol., 2018

The computation of conformal map by harmonic diffeomorphisms between surfaces.
Appl. Math. Comput., 2018

2017
Lossy compression on IoT big data by exploiting spatiotemporal correlation.
Proceedings of the 2017 IEEE High Performance Extreme Computing Conference, 2017

Network intrusion detection using word embeddings.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Understanding the impact of lossy compressions on IoT smart farm analytics.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Invited talk: Developing deep multi-source intelligent learning that facilitates the advancement of single cell genomics research.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017


  Loading...