Tianjin Huang

According to our database, Tianjin Huang authored at least 35 papers between 2016 and 2025.

Collaborative distances:

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

2025

LOST: Low-rank and Sparse Pre-training for Large Language Models.

Jiaxi Li, Lu Yin, Li Shen, Jinjin Xu, Liwu Xu, Tianjin Huang, Wenwu Wang, Shiwei Liu, Xilu Wang

CoRR, August, 2025

Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models.

Liangyu Wang, Huanyi Xie, Xinhai Wang, Tianjin Huang, Mengdi Li, Di Wang

CoRR, June, 2025

The Compositional Architecture of Regret in Large Language Models.

Xiangxiang Cui, Shu Yang, Tianjin Huang, Wanyu Lin, Lijie Hu, Di Wang

CoRR, June, 2025

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning.

Zihang Liu, Tianyu Pang, Oleg Balabanov, Chaoqun Yang, Tianjin Huang, Lu Yin, Yaoqing Yang, Shiwei Liu

CoRR, June, 2025

REOBench: Benchmarking Robustness of Earth Observation Foundation Models.

Xiang Li, Yong Tao, Siyuan Zhang, Siwei Liu, Zhitong Xiong, Chunbo Luo, Lu Liu, Mykola Pechenizkiy, Xiao Xiang Zhu, Tianjin Huang

CoRR, May, 2025

Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers.

Gaojie Jin, Tianjin Huang, Ronghui Mu, Xiaowei Huang

CoRR, March, 2025

Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam.

Tianjin Huang, Haotian Hu, Zhenyu Zhang, Gaojie Jin, Xiang Li, Li Shen, Tianlong Chen, Lu Liu, Qingsong Wen, Zhangyang Wang, Shiwei Liu

CoRR, February, 2025

Few-Shot Oriented Object Detection in Remote Sensing Images via Memorable Contrastive Learning.

Jiawei Zhou, Wuzhou Li, Yi Cao, Hongtao Cai, Tianjin Huang, Gui-Song Xia, Xiang Li

IEEE Trans. Geosci. Remote. Sens., 2025

FS-GNN: Improving Fairness in Graph Neural Networks via Joint Sparsification.

Jiaxu Zhao, Tianjin Huang, Shiwei Liu, Jie Yin, Yulong Pei, Meng Fang, Mykola Pechenizkiy

Neurocomputing, 2025

Traffic congestion predictor: A spatiotemporal graph neural network with congestion-conditional adaptive mechanism and optimization algorithm.

Yutian Liu, Jian Gong, Tianjin Huang, Jinde Cao

Expert Syst. Appl., 2025

Composable Interventions for Language Models.

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Enhancing Robust Fairness via Confusional Spectral Regularization.

Gaojie Jin, Sihao Wu, Jiaxu Liu, Tianjin Huang, Ronghui Mu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training.

Tianjin Huang, Ziquan Zhu, Gaojie Jin, Lu Liu, Zhangyang Wang, Shiwei Liu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

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

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

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

(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

Are Sparse Neural Networks Better Hard Sample Learners?

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

Proceedings of the 35th British Machine Vision Conference, 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

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