Beilun Wang
Orcid: 0000-0002-2646-1492
According to our database1,
Beilun Wang
authored at least 32 papers
between 2016 and 2023.
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Bibliography
2023
Proceedings of the ACM Web Conference 2023, 2023
Collaborative Estimating Multiple Gaussian Graphical Models on Resource Constrained Devices in IoT Networks.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023
Take CARE: Improving Inherent Robustness of Spiking Neural Networks with Channel-wise Activation Recalibration Module.
Proceedings of the IEEE International Conference on Data Mining, 2023
A Framework Using Absolute Compression Hard-Threshold for Improving The Robustness of Federated Learning Model.
Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design, 2023
Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design, 2023
Proceedings of the 26th International Conference on Computer Supported Cooperative Work in Design, 2023
Graph Inference via the Energy-efficient Dynamic Precision Matrix Estimation with One-bit Data.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
2022
TKGAT: Graph attention network for knowledge-enhanced tag-aware recommendation system.
Knowl. Based Syst., 2022
Fast and scalable learning of sparse changes in high-dimensional graphical model structure.
Neurocomputing, 2022
Proceedings of the International Conference on Machine Learning, 2022
2021
ACM Trans. Sens. Networks, 2021
Sensors, 2021
2020
How Decisions Are Made in Brains: Unpack "Black Box" of CNN With Ms. Pac-Man Video Game.
IEEE Access, 2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Proceedings of the 2020 IEEE International Conference on Knowledge Graph, 2020
2019
CoRR, 2019
2018
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models.
Proceedings of the 35th International Conference on Machine Learning, 2018
Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
A constrained $$\ell $$ ℓ 1 minimization approach for estimating multiple sparse Gaussian or nonparanormal graphical models.
Mach. Learn., 2017
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs.
CoRR, 2017
Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks.
Proceedings of the Biocomputing 2017: Proceedings of the Pacific Symposium, 2017
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples.
Proceedings of the 5th International Conference on Learning Representations, 2017
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples.
Proceedings of the 5th International Conference on Learning Representations, 2017
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
2016
Kernelized Information-Theoretic Metric Learning for Cancer Diagnosis Using High-Dimensional Molecular Profiling Data.
ACM Trans. Knowl. Discov. Data, 2016
A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models.
CoRR, 2016
A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise.
CoRR, 2016
Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks.
CoRR, 2016