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ORCIDTianjin Huang
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2020 – today
- 2025
[i22]Tianjin Huang, Ziquan Zhu, Gaojie Jin, Lu Liu, Zhangyang Wang, Shiwei Liu:
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training. CoRR abs/2501.06842 (2025)- [i21]Gaojie Jin, Sihao Wu, Jiaxu Liu, Tianjin Huang, Ronghui Mu:
Enhancing Robust Fairness via Confusional Spectral Regularization. CoRR abs/2501.13273 (2025) - [i20]Tianjin Huang, Haotian Hu, Zhenyu Zhang, Gaojie Jin, Xiang Li, Li Shen, Tianlong Chen, Lu Liu, Qingsong Wen, Zhangyang Wang, Shiwei Liu:
Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam. CoRR abs/2502.17055 (2025) - 2024
- [j4]Yutian Liu
, Soora Rasouli, Melvin Wong, Tao Feng, Tianjin Huang:
RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction. Inf. Fusion 102: 102078 (2024) - [i19]Arinbjörn Kolbeinsson, Kyle O'Brien, Tianjin Huang, Shanghua Gao, Shiwei Liu, Jonathan Richard Schwarz, Anurag Vaidya, Faisal Mahmood, Marinka Zitnik, Tianlong Chen, Thomas Hartvigsen:
Composable Interventions for Language Models. CoRR abs/2407.06483 (2024) - [i18]Tianjin Huang, Meng Fang, Li Shen, Fan Liu, Yulong Pei, Mykola Pechenizkiy, Shiwei Liu, Tianlong Chen:
(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork. CoRR abs/2407.17412 (2024) - [i17]Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu:
Are Sparse Neural Networks Better Hard Sample Learners? CoRR abs/2409.09196 (2024) - 2023
- [c13]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. AAAI 2023: 10945-10953 - [c12]Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Heterophily-Based Graph Neural Network for Imbalanced Classification. COMPLEX NETWORKS (1) 2023: 74-86 - [c11]Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Kumar Jaiswal, Zhangyang Wang:
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! ICLR 2023 - [c10]Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? ICML 2023: 14023-14038 - [c9]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. NeurIPS 2023 - [c8]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. ECML/PKDD (1) 2023: 113-130 - [i16]Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Jaiswal, Zhangyang Wang:
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! CoRR abs/2303.02141 (2023) - [i15]Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu:
Are Large Kernels Better Teachers than Transformers for ConvNets? CoRR abs/2305.19412 (2023) - [i14]Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu:
Dynamic Sparsity Is Channel-Level Sparsity Learner. CoRR abs/2305.19454 (2023) - [i13]Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy:
Enhancing Adversarial Training via Reweighting Optimization Trajectory. CoRR abs/2306.14275 (2023) - [i12]Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy:
Heterophily-Based Graph Neural Network for Imbalanced Classification. CoRR abs/2310.08725 (2023) - [i11]Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen:
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. CoRR abs/2312.01397 (2023) - [i10]Tianjin Huang, Tianlong Chen, Zhangyang Wang, Shiwei Liu:
The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need. CoRR abs/2312.05695 (2023) - 2022
- [j3]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-aggregated Attack for Transferable Adversarial Examples. ACM J. Emerg. Technol. Comput. Syst. 18(3): 60:1-60:22 (2022) - [j2]Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks. Mach. Learn. 111(2): 519-541 (2022) - [c7]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG 2022: 8 - [c6]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. ECML/PKDD (1) 2022: 225-241 - [c5]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy:
Superposing many tickets into one: A performance booster for sparse neural network training. UAI 2022: 2267-2277 - [i9]Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu:
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR abs/2205.15322 (2022) - [i8]Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy:
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. CoRR abs/2208.10842 (2022) - [i7]Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu:
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR abs/2211.15335 (2022) - 2021
- [c4]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
calibrated adversarial training. ACML 2021: 626-641 - [c3]Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. DSAA 2021: 1-2 - [c2]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. ECML/PKDD (2) 2021: 367-382 - [i6]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks. CoRR abs/2104.07917 (2021) - [i5]Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy:
Direction-Aggregated Attack for Transferable Adversarial Examples. CoRR abs/2104.09172 (2021) - [i4]Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy:
On Generalization of Graph Autoencoders with Adversarial Training. CoRR abs/2107.02658 (2021) - [i3]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Calibrated Adversarial Training. CoRR abs/2110.00623 (2021) - 2020
- [i2]Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy:
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks. CoRR abs/2009.14738 (2020) - [i1]Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy:
Bridging the Performance Gap between FGSM and PGD Adversarial Training. CoRR abs/2011.05157 (2020)
2010 – 2019
- 2017
- [j1]Tianjin Huang, Li Jia, Massimo Menenti, Jing Lu, Jie Zhou, Guangcheng Hu:
A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat - GLAS Footprints. Sensors 17(8): 1803 (2017) - 2016
- [c1]Tianjin Huang, Li Jia, Jing Lu, Jie Zhou:
An improved method of using icesat altimetry data to extract Tibetan Plateau glacier thickness change rate. IGARSS 2016: 7109-7112
Coauthor Index
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