Shiwei Liu
45 papers · 2020–2025 · 9 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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πΊοΈ Taxonomy Completionist (48) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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(2)
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(18)
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(20)
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The Questioner
(4)
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(7)
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Venues
Top co-authors
Research topics
Machine Learning
(39)
Deep Learning
(28)
Application Areas
(27)
Artificial Intelligence
(20)
Core AI
(18)
Optimization & Theory Β· Machine Learning
(18)
Techniques
(12)
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Optimization & Theory Β· Deep Learning
(10)
Natural Language Processing
(8)
Resources & Methods
(8)
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(7)
Core Methods
(6)
Learning Paradigms
(4)
Computer Vision
(3)
Keywords
model compression
(15)
sparse neural network
(8)
neural network pruning
(8)
network pruning
(6)
dynamic sparse training
(5)
large language model
(4)
neural network optimization
(4)
sparse training
(4)
lottery ticket hypothesis
(4)
sparse network
(3)
model pruning
(3)
convolutional neural network
(3)
dynamic sparsity
(3)
iterative magnitude pruning
(2)
attention score
(2)
magnitude pruning
(2)
pruning at initialization
(2)
ramanujan graph
(2)
neural network sparsification
(2)
self-supervised learning
(2)
Papers
Composable Interventions for Language Models
ICLR 2025
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
ICML 2025
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
ICML 2025
Mask-Enhanced Autoregressive Prediction: Pay Less Attention to Learn More
ICML 2025
Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN
ICLR 2025
Outlier-weighed Layerwise Sampling for LLM Fine-tuning
ACL 2025
SIDE: Socially Informed Drought Estimation Toward Understanding Societal Impact Dynamics of Environmental Crisis
AAAI 2025
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
ICLR 2025
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
AAAI 2025
AdaMerging: Adaptive Model Merging for Multi-Task Learning
ICLR 2024
Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
ICML 2024
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
NeurIPS 2024
CaM: Cache Merging for Memory-efficient LLMs Inference
ICML 2024
Dynamic Data Pruning for Automatic Speech Recognition
Interspeech 2024
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
ICLR 2024
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
NeurIPS 2024
FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping
EMNLP 2024
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
NeurIPS 2024
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
EMNLP 2024
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs βDifficult" Downstream Tasks in LLMs
ICML 2024
MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization
Interspeech 2024
NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization
ICLR 2024
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
ICML 2024
Sparse Cocktail: Every Sparse Pattern Every Sparse Ratio All At Once
ICML 2024
Are Large Kernels Better Teachers than Transformers for ConvNets?
ICML 2023
Data Augmented Flatness-aware Gradient Projection for Continual Learning
ICCV 2023
Donβt just prune by magnitude! Your mask topology is a secret weapon
NeurIPS 2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
NeurIPS 2023
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
ICML 2023
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
ICML 2023
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
AAAI 2023
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
ICLR 2023
Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model
NeurIPS 2023
REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH
ICLR 2023
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers
ICLR 2023
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
ICLR 2023
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
NeurIPS 2023
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
NeurIPS 2023
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
ICLR 2022
Dynamic Sparse Network for Time Series Classification: Learning What to βSeeβ
NeurIPS 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
ICLR 2022
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
ICML 2021
Selfish Sparse RNN Training
ICML 2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
NeurIPS 2021
Learning Sparse Neural Networks for Better Generalization
IJCAI 2020