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ORCIDNairouz Mrabah
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2020 – today
- 2025
[j6]Amal Shaheen
, Nairouz Mrabah, Riadh Ksantini, Abdulla Alqaddoumi:
Rethinking deep clustering paradigms: Self-supervision is all you need. Neural Networks 181: 106773 (2025)- 2024
- [j5]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
A contrastive variational graph auto-encoder for node clustering. Pattern Recognit. 149: 110209 (2024) - [j4]Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa:
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding. IEEE Trans. Knowl. Data Eng. 36(4): 1624-1637 (2024) - 2023
- [j3]Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini:
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering. IEEE Trans. Knowl. Data Eng. 35(9): 9037-9053 (2023) - [c6]Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo:
Exploring the Interaction between Local and Global Latent Configurations for Clustering Single-Cell RNA-Seq: A Unified Perspective. AAAI 2023: 9235-9242 - [c5]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Adversarial Deep Embedded Clustering: On a better trade-off between Feature Randomness and Feature Drift (Extended abstract). ICDE 2023: 3887-3888 - [c4]Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini:
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering (Extended abstract). ICDE 2023: 3891-3892 - [c3]Nairouz Mrabah, Mohamed Mahmoud Amar, Mohamed Bouguessa, Abdoulaye Baniré Diallo:
Toward Convex Manifolds: A Geometric Perspective for Deep Graph Clustering of Single-cell RNA-seq Data. IJCAI 2023: 4855-4863 - [c2]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Beyond The Evidence Lower Bound: Dual Variational Graph Auto-Encoders For Node Clustering. SDM 2023: 100-108 - [i5]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
A Contrastive Variational Graph Auto-Encoder for Node Clustering. CoRR abs/2312.16830 (2023) - [i4]Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa:
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding. CoRR abs/2312.16834 (2023) - 2022
- [j2]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Adversarial Deep Embedded Clustering: On a Better Trade-off Between Feature Randomness and Feature Drift. IEEE Trans. Knowl. Data Eng. 34(4): 1603-1617 (2022) - [c1]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Escaping Feature Twist: A Variational Graph Auto-Encoder for Node Clustering. IJCAI 2022: 3351-3357 - 2021
- [i3]Nairouz Mrabah, Mohamed Bouguessa, Mohamed Fawzi Touati, Riadh Ksantini:
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering. CoRR abs/2107.08562 (2021) - 2020
- [j1]Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini, Zied Lachiri:
Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction. Neural Networks 130: 206-228 (2020)
2010 – 2019
- 2019
- [i2]Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini:
Deep Clustering with a Dynamic Autoencoder. CoRR abs/1901.07752 (2019) - [i1]Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini:
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift. CoRR abs/1909.11832 (2019)
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