Intrinsically Sparse Long Short-Term Memory Networks. - Shiwei Liu 003; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2019)

On improving deep learning generalization with adaptive sparse connectivity. - Shiwei Liu 003; Decebal Constantin Mocanu; Mykola Pechenizkiy (CoRR, 2019)

Network Performance Optimization with Real Time Traffic Prediction in Data Center Network. - Fulong Yan; Shiwei Liu 003; Nicola Calabretta (ECOC, 2020)

Learning Sparse Neural Networks for Better Generalization. - Shiwei Liu 003 (IJCAI, 2020)

Topological Insights into Sparse Neural Networks. - Shiwei Liu 003; Tim van der Lee; Anil Yaman; Zahra Atashgahi; Davide Ferraro; Ghada Sokar; Mykola Pechenizkiy; Decebal Constantin Mocanu (ECML/PKDD (3), 2020)

Topological Insights in Sparse Neural Networks. - Shiwei Liu 003; Tim van der Lee; Anil Yaman; Zahra Atashgahi; Davide Ferraro; Ghada Sokar; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2020)

Hierarchical Semantic Segmentation using Psychometric Learning. - Lu Yin 006; Vlado Menkovski; Shiwei Liu 003; Mykola Pechenizkiy (ACML, 2021)

Selfish Sparse RNN Training. - Shiwei Liu 003; Decebal Constantin Mocanu; Yulong Pei; Mykola Pechenizkiy (ICML, 2021)

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training. - Shiwei Liu 003; Lu Yin 006; Decebal Constantin Mocanu; Mykola Pechenizkiy (ICML, 2021)

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen; Zahra Atashgahi; Lu Yin 006; Huanyu Kou; Li Shen 008; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (NeurIPS, 2021)

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (CoRR, 2021)

Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. - Shiwei Liu 003; Decebal Constantin Mocanu; Amarsagar Reddy Ramapuram Matavalam; Yulong Pei; Mykola Pechenizkiy (Neural Comput. Appl., 2021)

Efficient and effective training of sparse recurrent neural networks. - Shiwei Liu 003; Iftitahu Ni'mah; Vlado Menkovski; Decebal Constantin Mocanu; Mykola Pechenizkiy (Neural Comput. Appl., 2021)

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (ICLR, 2022)

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen; Li Shen 008; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy (ICLR, 2022)

You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets. - Tianjin Huang; Tianlong Chen 001; Meng Fang; Vlado Menkovski; Jiaxu Zhao 002; Lu Yin 006; Yulong Pei; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy; Shiwei Liu 003 (LoG, 2022)

Dynamic Sparse Network for Time Series Classification: Learning What to "See". - Qiao Xiao; Boqian Wu; Yu Zhang 006; Shiwei Liu 003; Mykola Pechenizkiy; Elena Mocanu; Decebal Constantin Mocanu (NeurIPS, 2022)

Achieving Personalized Federated Learning with Sparse Local Models. - Tiansheng Huang; Shiwei Liu 003; Li Shen 008; Fengxiang He; Weiwei Lin 001; Dacheng Tao (CoRR, 2022)

Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training. - Lu Yin 006; Vlado Menkovski; Meng Fang; Tianjin Huang; Yulong Pei; Mykola Pechenizkiy; Decebal Constantin Mocanu; Shiwei Liu 003 (CoRR, 2022)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen; Xuxi Chen; Qiao Xiao; Boqian Wu; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (CoRR, 2022)

A brain-inspired algorithm for training highly sparse neural networks. - Zahra Atashgahi; Joost Pieterse; Shiwei Liu 003; Decebal Constantin Mocanu; Raymond N. J. Veldhuis; Mykola Pechenizkiy (Mach. Learn., 2022)

Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost. - Lu Yin 006; Shiwei Liu 003; Meng Fang; Tianjin Huang; Vlado Menkovski; Mykola Pechenizkiy (AAAI, 2023)

Many-Task Federated Learning: A New Problem Setting and A Simple Baseline. - Ruisi Cai; Xiaohan Chen; Shiwei Liu 003; Jayanth Srinivasa; Myungjin Lee; Ramana Kompella; Zhangyang Wang (CVPR Workshops, 2023)

Data Augmented Flatness-aware Gradient Projection for Continual Learning. - Enneng Yang; Li Shen 008; Zhenyi Wang 001; Shiwei Liu 003; Guibing Guo; Xingwei Wang 001 (ICCV, 2023)

Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers. - Tianlong Chen 001; Zhenyu Zhang 015; Ajay Kumar Jaiswal; Shiwei Liu 003; Zhangyang Wang (ICLR, 2023)

Revisiting Pruning at Initialization Through the Lens of Ramanujan Graph. - Duc N. M. Hoang; Shiwei Liu 003; Radu Marculescu; Zhangyang Wang (ICLR, 2023)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen; Xuxi Chen; Qiao Xiao; Boqian Wu; Tommi Kärkkäinen; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (ICLR, 2023)

Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together! - Shiwei Liu 003; Tianlong Chen 001; Zhenyu Zhang 015; Xuxi Chen; Tianjin Huang; Ajay Kumar Jaiswal; Zhangyang Wang (ICLR, 2023)

Are Large Kernels Better Teachers than Transformers for ConvNets? - Tianjin Huang; Lu Yin 006; Zhenyu Zhang 015; Li Shen 008; Meng Fang; Mykola Pechenizkiy; Zhangyang Wang; Shiwei Liu 003 (ICML, 2023)

Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication. - Ajay Kumar Jaiswal; Shiwei Liu 003; Tianlong Chen 001; Ying Ding 001; Zhangyang Wang (ICML, 2023)

Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models. - Ajay Kumar Jaiswal; Shiwei Liu 003; Tianlong Chen 001; Ying Ding 001; Zhangyang Wang (ICML, 2023)

Dynamic Sparsity Is Channel-Level Sparsity Learner. - Lu Yin 006; Gen Li 012; Meng Fang; Li Shen 008; Tianjin Huang; Zhangyang Wang; Vlado Menkovski; Xiaolong Ma; Mykola Pechenizkiy; Shiwei Liu 003 (NeurIPS, 2023)

Don't just prune by magnitude! Your mask topology is a secret weapon. - Duc Hoang; Souvik Kundu 009; Shiwei Liu 003; Zhangyang Wang (NeurIPS, 2023)

The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter. - Ajay Jaiswal; Shiwei Liu 003; Tianlong Chen 001; Zhangyang Wang (NeurIPS, 2023)

Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? - Hoang Pham; The-Anh Ta; Shiwei Liu 003; Lichuan Xiang; Dung Le; Hongkai Wen 001; Long Tran-Thanh (NeurIPS, 2023)

Enhancing Adversarial Training via Reweighting Optimization Trajectory. - Tianjin Huang; Shiwei Liu 003; Tianlong Chen 001; Meng Fang; Li Shen 008; Vlado Menkovski; Lu Yin 006; Yulong Pei; Mykola Pechenizkiy (ECML/PKDD (1), 2023)

REST: Enhancing Group Robustness in DNNs Through Reweighted Sparse Training. - Jiaxu Zhao 002; Lu Yin 006; Shiwei Liu 003; Meng Fang; Mykola Pechenizkiy (ECML/PKDD (2), 2023)

Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers. - Shiwei Liu 003; Zhangyang Wang (CoRR, 2023)

Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity. - Lu Yin 006; Shiwei Liu 003; Ajay Jaiswal; Souvik Kundu 009; Zhangyang Wang (CoRR, 2023)

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. - Lu Yin 006; You Wu 001; Zhenyu Zhang 015; Cheng-Yu Hsieh; Yaqing Wang 007; Yiling Jia; Mykola Pechenizkiy; Yi Liang; Zhangyang Wang; Shiwei Liu 003 (CoRR, 2023)

The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need. - Tianjin Huang; Tianlong Chen 001; Zhangyang Wang; Shiwei Liu 003 (CoRR, 2023)

Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. - Zahra Atashgahi; Xuhao Zhang; Neil Kichler; Shiwei Liu 003; Lu Yin 006; Mykola Pechenizkiy; Raymond N. J. Veldhuis; Decebal Constantin Mocanu (Trans. Mach. Learn. Res., 2023)

Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance. - Shiwei Liu 003; Yuesong Tian; Tianlong Chen 001; Li Shen 008 (Int. J. Comput. Vis., 2023)

Sparse Sounds: Exploring Low-Dimensionality in Music Generation Model. - Shu Wang; Shiwei Liu 003 (IEEE Big Data, 2024)

HRBP: Hardware-friendly Regrouping towards Block-based Pruning for Sparse CNN Training. - Haoyu Ma; Chengming Zhang 006; Lizhi Xiang; Xiaolong Ma; Geng Yuan; Wenkai Zhang; Shiwei Liu 003; Tianlong Chen 001; Dingwen Tao; Yanzhi Wang; Zhangyang Wang; Xiaohui Xie (CPAL, 2024)

Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning. - Abhinav Bandari; Lu Yin 006; Cheng-Yu Hsieh; Ajay Jaiswal; Tianlong Chen 001; Li Shen 008; Ranjay Krishna; Shiwei Liu 003 (EMNLP, 2024)

FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping. - Ajay Jaiswal; Bodun Hu; Lu Yin 006; Yeonju Ro; Tianlong Chen 001; Shiwei Liu 003; Aditya Akella (EMNLP, 2024)

Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs. - Yuxin Zhang 002; Lirui Zhao; Mingbao Lin; Yunyun Sun; Yiwu Yao; Xingjia Han; Jared Tanner; Shiwei Liu 003; Rongrong Ji (ICLR, 2024)

NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization. - Gen Li 012; Lu Yin 006; Jie Ji; Wei Niu 002; Minghai Qin; Bin Ren; Linke Guo; Shiwei Liu 003; Xiaolong Ma (ICLR, 2024)

AdaMerging: Adaptive Model Merging for Multi-Task Learning. - Enneng Yang; Zhenyi Wang 001; Li Shen 008; Shiwei Liu 003; Guibing Guo; Xingwei Wang 001; Dacheng Tao (ICLR, 2024)

CaM: Cache Merging for Memory-efficient LLMs Inference. - Yuxin Zhang 002; Yuxuan Du; Gen Luo; Yunshan Zhong; Zhenyu Zhang 015; Shiwei Liu 003; Rongrong Ji (ICML, 2024)

Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs. - Lu Yin 006; Ajay Kumar Jaiswal; Shiwei Liu 003; Souvik Kundu 009; Zhangyang Wang (ICML, 2024)

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity. - Lu Yin 006; You Wu 001; Zhenyu Zhang 015; Cheng-Yu Hsieh; Yaqing Wang 007; Yiling Jia; Gen Li 012; Ajay Kumar Jaiswal; Mykola Pechenizkiy; Yi Liang; Michael Bendersky; Zhangyang Wang; et al. (ICML, 2024)

Advancing Dynamic Sparse Training by Exploring Optimization Opportunities. - Jie Ji; Gen Li 012; Lu Yin 006; Minghai Qin; Geng Yuan; Linke Guo; Shiwei Liu 003; Xiaolong Ma (ICML, 2024)

Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once. - Zhangheng Li; Shiwei Liu 003; Tianlong Chen 001; Ajay Kumar Jaiswal; Zhenyu Zhang 015; Dilin Wang; Raghuraman Krishnamoorthi; Shiyu Chang; Zhangyang Wang (ICML, 2024)

MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization. - Adriana Fernandez-Lopez; Honglie Chen; Pingchuan Ma 001; Lu Yin 006; Qiao Xiao; Stavros Petridis; Shiwei Liu 003; Maja Pantic (INTERSPEECH, 2024)

Dynamic Data Pruning for Automatic Speech Recognition. - Qiao Xiao; Pingchuan Ma 001; Adriana Fernandez-Lopez; Boqian Wu; Lu Yin 006; Stavros Petridis; Mykola Pechenizkiy; Maja Pantic; Decebal Constantin Mocanu; Shiwei Liu 003 (INTERSPEECH, 2024)

Q-Hitter: A Better Token Oracle for Efficient LLM Inference via Sparse-Quantized KV Cache. - Zhenyu Zhang 015; Shiwei Liu 003; Runjin Chen; Bhavya Kailkhura; Beidi Chen; Atlas Wang (MLSys, 2024)

Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding. - Zhenyu Zhang 015; Runjin Chen; Shiwei Liu 003; Zhewei Yao; Olatunji Ruwase; Beidi Chen; Xiaoxia Wu; Zhangyang Wang (NeurIPS, 2024)

AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models. - Haiquan Lu; Yefan Zhou; Shiwei Liu 003; Zhangyang Wang; Michael W. Mahoney; Yaoqing Yang (NeurIPS, 2024)

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation. - Boqian Wu; Qiao Xiao; Shiwei Liu 003; Lu Yin 006; Mykola Pechenizkiy; Decebal Constantin Mocanu; Maurice van Keulen; Elena Mocanu (NeurIPS, 2024)

OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning. - Pengxiang Li; Lu Yin 006; Xiaowei Gao; Shiwei Liu 003 (CoRR, 2024)

Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion. - Anke Tang; Li Shen 008; Yong Luo 002; Shiwei Liu 003; Han Hu 003; Bo Du 001 (CoRR, 2024)

From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients. - Ajay Jaiswal; Lu Yin 006; Zhenyu Zhang 015; Shiwei Liu 003; Jiawei Zhao; Yuandong Tian; Zhangyang Wang (CoRR, 2024)

(PASS) Visual Prompt Locates Good Structure Sparsity through a Recurrent HyperNetwork. - Tianjin Huang; Meng Fang; Li Shen 008; Fan Liu; Yulong Pei; Mykola Pechenizkiy; Shiwei Liu 003; Tianlong Chen 001 (CoRR, 2024)

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning. - Mingyu Cao; Gen Li 012; Jie Ji; Jiaqi Zhang; Xiaolong Ma; Shiwei Liu 003; Lu Yin 006 (CoRR, 2024)

Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective. - Can Jin; Tianjin Huang; Yihua Zhang; Mykola Pechenizkiy; Sijia Liu 001; Shiwei Liu 003; Tianlong Chen 001 (AAAI, 2025)

Outlier-weighed Layerwise Sampling for LLM Fine-tuning. - Pengxiang Li; Lu Yin 006; Xiaowei Gao; Shiwei Liu 003 (ACL (Findings), 2025)

Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients. - Zhenyu Zhang 015; Ajay Kumar Jaiswal; Lu Yin 006; Shiwei Liu 003; Jiawei Zhao; Yuandong Tian; Zhangyang Wang (CPAL, 2025)

Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models. - Adriana Fernandez-Lopez; Shiwei Liu 003; Lu Yin 006; Stavros Petridis; Maja Pantic (ICASSP, 2025)

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training. - Tianjin Huang; Ziquan Zhu; Gaojie Jin; Lu Liu; Zhangyang Wang; Shiwei Liu 003 (ICLR, 2025)

Composable Interventions for Language Models. - Arinbjörn Kolbeinsson; Kyle O'Brien; Tianjin Huang; Shanghua Gao; Shiwei Liu 003; Jonathan Richard Schwarz; Anurag Jayant Vaidya; Faisal Mahmood 001; Marinka Zitnik; Tianlong Chen 001; Thomas Hartvigsen (ICLR, 2025)

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN. - Pengxiang Li; Lu Yin 006; Shiwei Liu 003 (ICLR, 2025)

FS-GNN: Improving Fairness in Graph Neural Networks via Joint Sparsification. - Jiaxu Zhao 002; Tianjin Huang; Shiwei Liu 003; Jie Yin 001; Yulong Pei; Meng Fang; Mykola Pechenizkiy (Neurocomputing, 2025)

O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning. - Haotian Luo; Li Shen 008; Haiying He; Yibo Wang; Shiwei Liu 003; Wei Li 190; Naiqiang Tan; Xiaochun Cao; Dacheng Tao (CoRR, 2025)

The Curse of Depth in Large Language Models. - Wenfang Sun; Xinyuan Song; Pengxiang Li; Lu Yin 006; Yefeng Zheng 001; Shiwei Liu 003 (CoRR, 2025)

Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More. - Xialie Zhuang; Zhikai Jia; Jianjin Li; Zhenyu Zhang 015; Li Shen 008; Zheng Cao; Shiwei Liu 003 (CoRR, 2025)

Stable-SPAM: How to Train in 4-Bit More Stably than 16-Bit Adam. - Tianjin Huang; Haotian Hu; Zhenyu Zhang 015; Gaojie Jin; Xiang Li 001; Li Shen 008; Tianlong Chen 001; Lu Liu; Qingsong Wen; Zhangyang Wang; Shiwei Liu 003 (CoRR, 2025)

SoS1: O1 and R1-Like Reasoning LLMs are Sum-of-Square Solvers. - Kechen Li; Wenqi Zhu; Coralia Cartis; Tianbo Ji; Shiwei Liu 003 (CoRR, 2025)

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling. - Bram Grooten; Farid Hasanov; Chenxiang Zhang; Qiao Xiao; Boqian Wu; Zahra Atashgahi; Ghada Sokar; Shiwei Liu 003; Lu Yin 006; Elena Mocanu; Mykola Pechenizkiy; Decebal Constantin Mocanu (CoRR, 2025)

Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution. - Qiao Xiao; Alan Ansell; Boqian Wu; Lu Yin 006; Mykola Pechenizkiy; Shiwei Liu 003; Decebal Constantin Mocanu (CoRR, 2025)

Revisiting Flatness-Aware Optimization in Continual Learning With Orthogonal Gradient Projection. - Enneng Yang; Li Shen 008; Zhenyi Wang 001; Shiwei Liu 003; Guibing Guo; Xing-wei Wang 001; Dacheng Tao (IEEE Trans. Pattern Anal. Mach. Intell., 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 006; Yaoqing Yang; Shiwei Liu 003 (CoRR, 2025)

A Technical Study into 0.5B Reasoning Language Models. - Xialie Zhuang; Peixian Ma; Zhikai Jia; Shiwei Liu 003; Zheng Cao (CoRR, 2025)

AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs. - Di He; Ajay Jaiswal; Songjun Tu; Li Shen 008; Ganzhao Yuan; Shiwei Liu 003; Lu Yin 006 (CoRR, 2025)

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching. - Guinan Su; Li Shen 008; Lu Yin 006; Shiwei Liu 003; Yanwu Yang; Jonas Geiping (CoRR, 2025)

Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning. - Xin Xu; Tianlong Chen 001; Fan Zhang; Wanlong Liu; Pengxiang Li; Ajay Kumar Jaiswal; Yuchen Yan; Jishan Hu; Yang Wang 020; Hao Chen; Shiwei Liu 003; Shizhe Diao; et al. (CoRR, 2025)

GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling. - Tianlong Chen 001; Xin Xu; Zijing Liu; Pengxiang Li; Xinyuan Song; Ajay Kumar Jaiswal; Fan Zhang; Jishan Hu; Yang Wang 020; Hao Chen; Shizhe Diao; Shiwei Liu 003; et al. (CoRR, 2025)

LOST: Low-rank and Sparse Pre-training for Large Language Models. - Jiaxi Li; Lu Yin 006; Li Shen 008; Jinjin Xu; Liwu Xu; Tianjin Huang; Wenwu Wang 001; Shiwei Liu 003; Xilu Wang 001 (CoRR, 2025)

Data-Adaptive Weight-Ensembling for Multi-task Model Fusion. - Anke Tang; Li Shen 008; Yong Luo 002; Shiwei Liu 003; Han Hu 003; Bo Du 001; Dacheng Tao (Int. J. Comput. Vis., 2025)