Tianjin Huang

According to our database, Tianjin Huang authored at least 23 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction.

Yutian Liu, Soora Rasouli, Melvin Wong, Tao Feng, Tianjin Huang

Inf. Fusion, February, 2024

Are Sparse Neural Networks Better Hard Sample Learners?

Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu

CoRR, 2024

(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork.

Tianjin Huang, Meng Fang, Li Shen, Fan Liu, Yulong Pei, Mykola Pechenizkiy, Shiwei Liu, Tianlong Chen

CoRR, 2024

Composable Interventions for Language Models.

Arinbjorn Kolbeinsson, Kyle O'Brien, Tianjin Huang, Shanghua Gao, Shiwei Liu, Jonathan Richard Schwarz, Anurag Vaidya, Faisal Mahmood, Marinka Zitnik, Tianlong Chen, Thomas Hartvigsen

CoRR, 2024

2023

The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need.

Tianjin Huang, Tianlong Chen, Zhangyang Wang, Shiwei Liu

CoRR, 2023

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective.

Can Jin, Tianjin Huang, Yihua Zhang, Mykola Pechenizkiy, Sijia Liu, Shiwei Liu, Tianlong Chen

CoRR, 2023

Enhancing Adversarial Training via Reweighting Optimization Trajectory.

Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Dynamic Sparsity Is Channel-Level Sparsity Learner.

Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Are Large Kernels Better Teachers than Transformers for ConvNets?

Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu

Proceedings of the International Conference on Machine Learning, 2023

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Kumar Jaiswal, Zhangyang Wang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Heterophily-Based Graph Neural Network for Imbalanced Classification.

Zirui Liang, Yuntao Li, Tianjin Huang, Akrati Saxena, Yulong Pei, Mykola Pechenizkiy

Proceedings of the Complex Networks & Their Applications XII, 2023

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost.

Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks.

Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy

Mach. Learn., 2022

Direction-aggregated Attack for Transferable Adversarial Examples.

Tianjin Huang, Vlado Menkovski, Yulong Pei, Yuhao Wang, Mykola Pechenizkiy

ACM J. Emerg. Technol. Comput. Syst., 2022

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training.

Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu

CoRR, 2022

Superposing many tickets into one: A performance booster for sparse neural network training.

Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks.

Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets.

Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu

Proceedings of the Learning on Graphs Conference, 2022

2021

On Generalization of Graph Autoencoders with Adversarial Training.

Tianjin Huang, Yulong Pei, Vlado Menkovski, Mykola Pechenizkiy

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

calibrated adversarial training.

Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Bridging the Performance Gap between FGSM and PGD Adversarial Training.

Tianjin Huang, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

CoRR, 2020

2017

A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat - GLAS Footprints.

Tianjin Huang, Li Jia, Massimo Menenti, Jing Lu, Jie Zhou, Guangcheng Hu

Sensors, 2017

2016

An improved method of using icesat altimetry data to extract Tibetan Plateau glacier thickness change rate.

Tianjin Huang, Li Jia, Jing Lu, Jie Zhou

Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016