| ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks Y Pei, T Huang, W van Ipenburg, M Pechenizkiy [DSAA] 2021 IEEE 8th International Conference on Data Science and Advanced …, 2021 | 67 | 2021 |
| Hop-count based self-supervised anomaly detection on attributed networks T Huang, Y Pei, V Menkovski, M Pechenizkiy ECML 2022, 2021 | 25 | 2021 |
| Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! S Liu, T Chen, Z Zhang, X Chen, T Huang, A Jaiswal, Z Wang ICLR 2023, 2023 | 24 | 2023 |
| You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets T Huang, T Chen, M Fang, V Menkovski, J Zhao, L Yin, Y Pei, DC Mocanu, ... LoG 2022 (Oral & Best Paper Award), 2022 | 17 | 2022 |
| Bridging the Performance Gap between FGSM and PGD Adversarial Training T Huang, V Menkovski, Y Pei, M Pechenizkiy arXiv preprint arXiv:2011.05157, 2020 | 16 | 2020 |
| Direction-aggregated attack for transferable adversarial examples T Huang, V Menkovski, Y Pei, Y Wang, M Pechenizkiy ACM Journal on Emerging Technologies in Computing Systems (JETC) 18 (3), 1-22, 2022 | 15 | 2022 |
| Visual prompting upgrades neural network sparsification: A data-model perspective C Jin, T Huang, Y Zhang, M Pechenizkiy, S Liu, S Liu, T Chen arXiv preprint arXiv:2312.01397, 2023 | 12 | 2023 |
| Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost L Yin, S Liu, F Meng, T Huang, V Menkovski, M Pechenizkiy AAAI 2023, 2022 | 10 | 2022 |
| Dynamic sparsity is channel-level sparsity learner L Yin, G Li, M Fang, L Shen, T Huang, Z Wang, V Menkovski, X Ma, ... Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
| Are Large Kernels Better Teachers than Transformers for ConvNets? T Huang, L Yin, Z Zhang, L Shen, M Fang, M Pechenizkiy, Z Wang, S Liu ICML 2023, 2023 | 9 | 2023 |
| Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training L Yin, V Menkovski, M Fang, T Huang, Y Pei, M Pechenizkiy, DC Mocanu, ... UAI 2022, 2022 | 7 | 2022 |
| RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction Y Liu, S Rasouli, M Wong, T Feng, T Huang* Information Fusion 102, 102078, 2024 | 6 | 2024 |
| On generalization of graph autoencoders with adversarial training T Huang, Y Pei, V Menkovski, M Pechenizkiy [ECML2021] Machine Learning and Knowledge Discovery in Databases. Research …, 2021 | 5 | 2021 |
| A new method to estimate changes in glacier surface elevation based on polynomial fitting of sparse ICESat—GLAS footprints T Huang, L Jia, M Menenti, J Lu, J Zhou, G Hu Sensors 17 (8), 1803, 2017 | 5 | 2017 |
| Enhancing Adversarial Training via Reweighting Optimization Trajectory T Huang, S Liu, T Chen, M Fang, L Shen, V Menkovski, L Yin, Y Pei, ... ECML 2023, 2023 | 2 | 2023 |
| Calibrated adversarial training T Huang, V Menkovski, Y Pei, M Pechenizkiy Asian Conference on Machine Learning, 626-641, 2021 | 2 | 2021 |
| You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained Graph Tickets T Huang, T Chen, M Fang, V Menkovski, J Zhao, L Yin, Y Pei, DC Mocanu, ... Learning on Graphs Conference (Oral & Best Paper Award), 0 | 1* | |
| Composable Interventions for Language Models A Kolbeinsson, K O'Brien, T Huang, S Gao, S Liu, JR Schwarz, A Vaidya, ... arXiv preprint arXiv:2407.06483, 2024 | | 2024 |
| The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need T Huang, T Chen, Z Wang, S Liu arXiv preprint arXiv:2312.05695, 2023 | | 2023 |
| Heterophily-Based Graph Neural Network for Imbalanced Classification Z Liang, Y Li, T Huang, A Saxena, Y Pei, M Pechenizkiy International Conference on Complex Networks and Their Applications, 74-86, 2023 | | 2023 |
Mykola PechenizkiyEindhoven University of TechnologyVerified email at tue.nl
