Lu Yin

According to our database, Lu Yin authored at least 34 papers between 2020 and 2024.

Collaborative distances:

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

2024

Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models.

Adriana Fernandez-Lopez, Shiwei Liu, Lu Yin, Stavros Petridis, Maja Pantic

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

CoRR, 2024

Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation.

Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy

CoRR, 2024

From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients.

Ajay Jaiswal, Lu Yin, Zhenyu Zhang, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang

CoRR, 2024

Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients.

Zhenyu Zhang, Ajay Jaiswal, Lu Yin, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang

CoRR, 2024

Dynamic Data Pruning for Automatic Speech Recognition.

Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez, Boqian Wu, Lu Yin, Stavros Petridis, Mykola Pechenizkiy, Maja Pantic, Decebal Constantin Mocanu, Shiwei Liu

CoRR, 2024

MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization.

Adriana Fernandez-Lopez, Honglie Chen, Pingchuan Ma, Lu Yin, Qiao Xiao, Stavros Petridis, Shiwei Liu, Maja Pantic

CoRR, 2024

OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning.

Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei Liu

CoRR, 2024

A Structural-Clustering Based Active Learning for Graph Neural Networks.

Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy

Proceedings of the Advances in Intelligent Data Analysis XXII, 2024

Advancing Dynamic Sparse Training by Exploring Optimization Opportunities.

Jie Ji, Gen Li, Lu Yin, Minghai Qin, Geng Yuan, Linke Guo, Shiwei Liu, Xiaolong Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Gen Li, Ajay Kumar Jaiswal, Mykola Pechenizkiy, Yi Liang, Michael Bendersky, Zhangyang Wang, Shiwei Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs.

Lu Yin, Ajay Kumar Jaiswal, Shiwei Liu, Souvik Kundu, Zhangyang Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization.

Gen Li, Lu Yin, Jie Ji, Wei Niu, Minghai Qin, Bin Ren, Linke Guo, Shiwei Liu, Xiaolong Ma

Proceedings of the Twelfth International Conference on Learning Representations, 2024

FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping.

Ajay Jaiswal, Bodun Hu, Lu Yin, Yeonju Ro, Tianlong Chen, Shiwei Liu, Aditya Akella

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning.

Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks.

Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu

Trans. Mach. Learn. Res., 2023

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation.

Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu

CoRR, 2023

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu

CoRR, 2023

Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity.

Lu Yin, Shiwei Liu, Ajay Jaiswal, Souvik Kundu, Zhangyang Wang

CoRR, 2023

REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training.

Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy

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

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

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

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

Semantic-Based Few-Shot Classification by Psychometric Learning.

Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

2021

Semantic-Based Few-Shot Learning by Interactive Psychometric Testing.

Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy

CoRR, 2021

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration.

Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training.

Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Proceedings of the 38th International Conference on Machine Learning, 2021

Hierarchical Semantic Segmentation using Psychometric Learning.

Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing.

Lu Yin, Vlado Menkovski, Mykola Pechenizkiy

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing.

Lu Yin

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020