Lu Yin - RankMe

95 papers A* 18A 4B 2C 1Misc 1Journal 61Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning. Trans. Mach. Learn. Res. Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang, Ajay Jaiswal, Li Shen, Xiaolong Ma, Shiwei Liu, Lu Yin
2026	J	`jnl`	Dual-Kernel Adapter: Expanding Spatial Horizons for Data-Constrained Medical Image Analysis. CoRR Ziquan Zhu, Hanruo Zhu, Siyuan Lu, Xiang Li, Yanda Meng, Gaojie Jin, Lu Yin, Lijie Hu, Di Wang, Lu Liu, Tianjin Huang
2026	J	`jnl`	Measuring tourism destination image from multimodal online reviews: a novel framework using deep learning approach. J. Inf. Technol. Tour. Xiaoli Wang, Chenxi Zhang, Zeshui Xu, Lu Yin
2026	J	`jnl`	Progressive Residual Warmup for Language Model Pretraining. CoRR Tianlong Chen, Xin Xu, Lu Yin, Hao Chen, Yang Wang, Shizhe Diao, Can Yang
2026	J	`jnl`	SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation. CoRR Vishal Thengane, Zhaochong An, Tianjin Huang, Son Lam Phung, Abdesselam Bouzerdoum, Lu Yin, Na Zhao, Xiatian Zhu
2026	J	`jnl`	Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding? CoRR Pengxiang Li, Dilxat Muhtar, Tianlong Chen, Lu Yin, Shiwei Liu
2025	J	`jnl`	AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs. CoRR Di He, Ajay Jaiswal, Songjun Tu, Li Shen, Ganzhao Yuan, Shiwei Liu, Lu Yin
2025	B	`conf`	Aspect-Based Few-Shot Learning. DS Tim van Engeland, Lu Yin, Vlado Menkovski
2025	J	`jnl`	CLIMB-3D: Continual Learning for Imbalanced 3D Instance Segmentation. CoRR Vishal Thengane, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Lu Yin, Xiatian Zhu, Salman Khan
2025	J	`jnl`	Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models. CoRR Zihan Wang, Rui Pan, Jiarui Yao, Robert Csordas, Linjie Li, Lu Yin, Jiajun Wu, Tong Zhang, Manling Li, Shiwei Liu
2025	J	`jnl`	Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning. CoRR Xinyuan Song, Keyu Wang, Pengxiang Li, Lu Yin, Shiwei Liu
2025	J	`jnl`	Diffusion Language Models Know the Answer Before Decoding. CoRR Pengxiang Li, Yefan Zhou, Dilxat Muhtar, Lu Yin, Shilin Yan, Li Shen, Yi Liang, Soroush Vosoughi, Shiwei Liu
2025	J	`jnl`	Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning. CoRR Xin Xu, Tianlong Chen, Fan Zhang, Wanlong Liu, Pengxiang Li, Ajay Kumar Jaiswal, Yuchen Yan, Jishan Hu, Yang Wang, Hao Chen, Shiwei Liu, Shizhe Diao, Can Yang, Lu Yin
2025	A*	`conf`	From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications. ICML Ajay Kumar Jaiswal, Yifan Wang, Lu Yin, Shiwei Liu, Runjin Chen, Jiawei Zhao, Ananth Grama, Yuandong Tian, Zhangyang Wang
2025	Misc	`conf`	Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models. ICASSP Adriana Fernandez-Lopez, Shiwei Liu, Lu Yin, Stavros Petridis, Maja Pantic
2025	J	`jnl`	GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching. CoRR Guinan Su, Li Shen, Lu Yin, Shiwei Liu, Yanwu Yang, Jonas Geiping
2025	—	`conf`	Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places. SIGSPATIAL/GIS Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Ilya Ilyankou, Junyuan Liu, Lu Yin, Weiming Huang, Natchapon Jongwiriyanurak
2025	J	`jnl`	Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places. CoRR Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Ilya Ilyankou, Junyuan Liu, Lu Yin, Weiming Huang, Natchapon Jongwiriyanurak
2025	A*	`conf`	LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning. ICML Zihang Liu, Tianyu Pang, Oleg Balabanov, Chaoqun Yang, Tianjin Huang, Lu Yin, Yaoqing Yang, Shiwei Liu
2025	J	`jnl`	LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning. CoRR Zihang Liu, Tianyu Pang, Oleg Balabanov, Chaoqun Yang, Tianjin Huang, Lu Yin, Yaoqing Yang, Shiwei Liu
2025	J	`jnl`	LOST: Low-rank and Sparse Pre-training for Large Language Models. CoRR Jiaxi Li, Lu Yin, Li Shen, Jinjin Xu, Liwu Xu, Tianjin Huang, Wenwu Wang, Shiwei Liu, Xilu Wang
2025	J	`jnl`	Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution. CoRR Qiao Xiao, Alan Ansell, Boqian Wu, Lu Yin, Mykola Pechenizkiy, Shiwei Liu, Decebal Constantin Mocanu
2025	A*	`conf`	Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN. ICLR Pengxiang Li, Lu Yin, Shiwei Liu
2025	J	`jnl`	Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing. CoRR Zihao Wang, Enneng Yang, Lu Yin, Shiwei Liu, Li Shen
2025	J	`jnl`	Multi-Channel Spatio-Temporal Data Fusion of 'Big' and 'Small' Network Data Using Transformer Networks. ISPRS Int. J. Geo Inf. Tao Cheng, Hao Chen, Xianghui Zhang, Xiaowei Gao, Lu Yin, Jianbin Jiao
2025	J	`jnl`	Multi-modal contrastive learning of urban space representations from POI data. Comput. Environ. Urban Syst. Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Lu Yin, Junyuan Liu
2025	J	`jnl`	NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling. CoRR Bram Grooten, Farid Hasanov, Chenxiang Zhang, Qiao Xiao, Boqian Wu, Zahra Atashgahi, Ghada Sokar, Shiwei Liu, Lu Yin, Elena Mocanu, Mykola Pechenizkiy, Decebal Constantin Mocanu
2025	—	`conf`	Outlier-weighed Layerwise Sampling for LLM Fine-tuning. ACL (Findings) Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei Liu
2025	J	`jnl`	Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning. Trans. Mach. Learn. Res. Andy Li, Aiden Durrant, Milan Markovic, Tianjin Huang, Souvik Kundu, Tianlong Chen, Lu Yin, Georgios Leontidis
2025	—	`conf`	Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients. CPAL Zhenyu Zhang, Ajay Kumar Jaiswal, Lu Yin, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang
2025	J	`jnl`	Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models. CoRR Guinan Su, Yanwu Yang, Li Shen, Lu Yin, Shiwei Liu, Jonas Geiping
2025	A*	`conf`	SEBRA : Debiasing through Self-Guided Bias Ranking. ICLR Adarsh Kappiyath, Abhra Chaudhuri, Ajay Kumar Jaiswal, Ziquan Liu, Yunpeng Li, Xiatian Zhu, Lu Yin
2025	J	`jnl`	Sebra: Debiasing Through Self-Guided Bias Ranking. CoRR Adarsh Kappiyath, Abhra Chaudhuri, Ajay Jaiswal, Ziquan Liu, Yunpeng Li, Xiatian Zhu, Lu Yin
2025	A*	`conf`	TODO: Enhancing LLM Alignment with Ternary Preferences. ICLR Yuxiang Guo, Lu Yin, Bo Jiang, Jiaqi Zhang
2025	J	`jnl`	The Curse of Depth in Large Language Models. CoRR Wenfang Sun, Xinyuan Song, Pengxiang Li, Lu Yin, Yefeng Zheng, Shiwei Liu
2025	J	`jnl`	When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs. CoRR Keyu Wang, Tian Lyu, Guinan Su, Jonas Geiping, Lu Yin, Marco Canini, Shiwei Liu
2024	—	`conf`	A Structural-Clustering Based Active Learning for Graph Neural Networks. IDA (1) Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy
2024	A*	`conf`	Advancing Dynamic Sparse Training by Exploring Optimization Opportunities. ICML Jie Ji, Gen Li, Lu Yin, Minghai Qin, Geng Yuan, Linke Guo, Shiwei Liu, Xiaolong Ma
2024	A	`conf`	Are Sparse Neural Networks Better Hard Sample Learners? BMVC Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu
2024	J	`jnl`	Are Sparse Neural Networks Better Hard Sample Learners? CoRR Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin Mocanu
2024	J	`jnl`	Aspect-Based Few-Shot Learning. CoRR Tim van Engeland, Lu Yin, Vlado Menkovski
2024	J	`jnl`	Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning. CoRR Mingyu Cao, Gen Li, Jie Ji, Jiaqi Zhang, Xiaolong Ma, Shiwei Liu, Lu Yin
2024	A	`conf`	Dynamic Data Pruning for Automatic Speech Recognition. INTERSPEECH Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez, Boqian Wu, Lu Yin, Stavros Petridis, Mykola Pechenizkiy, Maja Pantic, Decebal Constantin Mocanu, Shiwei Liu
2024	J	`jnl`	Dynamic Data Pruning for Automatic Speech Recognition. CoRR Qiao Xiao, Pingchuan Ma, Adriana Fernandez-Lopez, Boqian Wu, Lu Yin, Stavros Petridis, Mykola Pechenizkiy, Maja Pantic, Decebal Constantin Mocanu, Shiwei Liu
2024	A*	`conf`	E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. NeurIPS Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu
2024	A*	`conf`	FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping. EMNLP Ajay Jaiswal, Bodun Hu, Lu Yin, Yeonju Ro, Tianlong Chen, Shiwei Liu, Aditya Akella
2024	J	`jnl`	FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping. CoRR Ajay Jaiswal, Bodun Hu, Lu Yin, Yeonju Ro, Shiwei Liu, Tianlong Chen, Aditya Akella
2024	J	`jnl`	From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients. CoRR Ajay Jaiswal, Lu Yin, Zhenyu Zhang, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang
2024	J	`jnl`	Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models. CoRR Adriana Fernandez-Lopez, Shiwei Liu, Lu Yin, Stavros Petridis, Maja Pantic
2024	A*	`conf`	Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning. EMNLP Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu
2024	J	`jnl`	Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning. CoRR Abhinav Bandari, Lu Yin, Cheng-Yu Hsieh, Ajay Kumar Jaiswal, Tianlong Chen, Li Shen, Ranjay Krishna, Shiwei Liu
2024	A*	`conf`	Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs. ICML Lu Yin, Ajay Kumar Jaiswal, Shiwei Liu, Souvik Kundu, Zhangyang Wang
2024	A	`conf`	MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization. INTERSPEECH Adriana Fernandez-Lopez, Honglie Chen, Pingchuan Ma, Lu Yin, Qiao Xiao, Stavros Petridis, Shiwei Liu, Maja Pantic
2024	J	`jnl`	MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization. CoRR Adriana Fernandez-Lopez, Honglie Chen, Pingchuan Ma, Lu Yin, Qiao Xiao, Stavros Petridis, Shiwei Liu, Maja Pantic
2024	J	`jnl`	Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN. CoRR Pengxiang Li, Lu Yin, Shiwei Liu
2024	J	`jnl`	Multimodal Contrastive Learning of Urban Space Representations from POI Data. CoRR Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Lu Yin, Junyuan Liu
2024	A*	`conf`	NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization. ICLR Gen Li, Lu Yin, Jie Ji, Wei Niu, Minghai Qin, Bin Ren, Linke Guo, Shiwei Liu, Xiaolong Ma
2024	J	`jnl`	OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework. CoRR Jiaxi Li, Lu Yin, Xilu Wang
2024	A*	`conf`	Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. ICML 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
2024	J	`jnl`	OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning. CoRR Pengxiang Li, Lu Yin, Xiaowei Gao, Shiwei Liu
2024	J	`jnl`	Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning. CoRR Andy Li, Aiden Durrant, Milan Markovic, Lu Yin, Georgios Leontidis
2024	J	`jnl`	Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients. CoRR Zhenyu Zhang, Ajay Jaiswal, Lu Yin, Shiwei Liu, Jiawei Zhao, Yuandong Tian, Zhangyang Wang
2024	J	`jnl`	Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation. CoRR Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy
2024	J	`jnl`	TODO: Enhancing LLM Alignment with Ternary Preferences. CoRR Yuxiang Guo, Lu Yin, Bo Jiang, Jiaqi Zhang
2023	J	`jnl`	A Structural-Clustering Based Active Learning for Graph Neural Networks. CoRR Ricky Maulana Fajri, Yulong Pei, Lu Yin, Mykola Pechenizkiy
2023	A*	`conf`	Are Large Kernels Better Teachers than Transformers for ConvNets? ICML Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu
2023	J	`jnl`	Are Large Kernels Better Teachers than Transformers for ConvNets? CoRR Tianjin Huang, Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang Wang, Shiwei Liu
2023	A*	`conf`	Dynamic Sparsity Is Channel-Level Sparsity Learner. NeurIPS Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu
2023	J	`jnl`	Dynamic Sparsity Is Channel-Level Sparsity Learner. CoRR Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu
2023	J	`jnl`	E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. CoRR Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice van Keulen, Elena Mocanu
2023	—	`conf`	Enhancing Adversarial Training via Reweighting Optimization Trajectory. ECML/PKDD (1) Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy
2023	J	`jnl`	Enhancing Adversarial Training via Reweighting Optimization Trajectory. CoRR Tianjin Huang, Shiwei Liu, Tianlong Chen, Meng Fang, Li Shen, Vlado Menkovski, Lu Yin, Yulong Pei, Mykola Pechenizkiy
2023	J	`jnl`	Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity. CoRR Lu Yin, Shiwei Liu, Ajay Jaiswal, Souvik Kundu, Zhangyang Wang
2023	A*	`conf`	Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. AAAI Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy
2023	J	`jnl`	Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. CoRR Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh, Yaqing Wang, Yiling Jia, Mykola Pechenizkiy, Yi Liang, Zhangyang Wang, Shiwei Liu
2023	—	`conf`	REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training. ECML/PKDD (2) Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy
2023	J	`jnl`	REST: Enhancing Group Robustness in DNNs through Reweighted Sparse Training. CoRR Jiaxu Zhao, Lu Yin, Shiwei Liu, Meng Fang, Mykola Pechenizkiy
2023	J	`jnl`	Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. CoRR Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu
2023	J	`jnl`	Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. Trans. Mach. Learn. Res. Zahra Atashgahi, Xuhao Zhang, Neil Kichler, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu
2022	J	`jnl`	Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. CoRR Lu Yin, Shiwei Liu, Meng Fang, Tianjin Huang, Vlado Menkovski, Mykola Pechenizkiy
2022	B	`conf`	Semantic-Based Few-Shot Classification by Psychometric Learning. IDA Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy
2022	J	`jnl`	Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. CoRR Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu
2022	A	`conf`	Superposing many tickets into one: A performance booster for sparse neural network training. UAI Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy
2022	—	`conf`	You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. LoG Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu
2022	J	`jnl`	You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. CoRR Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu
2021	A*	`conf`	Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. ICML Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy
2021	J	`jnl`	Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. CoRR Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy
2021	C	`conf`	Hierarchical Semantic Segmentation using Psychometric Learning. ACML Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy
2021	J	`jnl`	Hierarchical Semantic Segmentation using Psychometric Learning. CoRR Lu Yin, Vlado Menkovski, Shiwei Liu, Mykola Pechenizkiy
2021	J	`jnl`	Semantic-Based Few-Shot Learning by Interactive Psychometric Testing. CoRR Lu Yin, Vlado Menkovski, Yulong Pei, Mykola Pechenizkiy
2021	A*	`conf`	Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. NeurIPS Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
2021	J	`jnl`	Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. CoRR Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu
2020	A*	`conf`	Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing. IJCAI Lu Yin
2020	—	`conf`	Knowledge Elicitation Using Deep Metric Learning and Psychometric Testing. ECML/PKDD (2) Lu Yin, Vlado Menkovski, Mykola Pechenizkiy
2020	J	`jnl`	Knowledge Elicitation using Deep Metric Learning and Psychometric Testing. CoRR Lu Yin, Vlado Menkovski, Mykola Pechenizkiy