Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
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2310.17157
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Published
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14
Pruning
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Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Paper • 2305.15805 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Paper • 2110.07560 • Published • 2 -
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
Paper • 2204.02292 • Published • 1 -
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Paper • 2210.04311 • Published • 1 -
LoRAPrune: Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Paper • 2305.18403 • Published • 2 -
Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling
Paper • 2305.08285 • Published • 1 -
Multi-Head Adapter Routing for Cross-Task Generalization
Paper • 2211.03831 • Published • 2 -
Improving Visual Prompt Tuning for Self-supervised Vision Transformers
Paper • 2306.05067 • Published • 2 -
Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation
Paper • 2308.01045 • Published • 1 -
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Paper • 2306.01705 • Published • 1 -
Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Paper • 2303.11525 • Published • 1 -
How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites
Paper • 1601.00720 • Published • 1 -
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Paper • 1707.04780 • Published • 1 -
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Paper • 2012.00560 • Published • 1 -
Sparse Finetuning for Inference Acceleration of Large Language Models
Paper • 2310.06927 • Published • 15 -
How Well Do Sparse Imagenet Models Transfer?
Paper • 2111.13445 • Published • 1 -
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
Paper • 2203.07259 • Published • 4 -
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot
Paper • 2301.00774 • Published • 3 -
PockEngine: Sparse and Efficient Fine-tuning in a Pocket
Paper • 2310.17752 • Published • 14 -
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
Paper • 2310.18356 • Published • 24 -
Continual Learning via Neural Pruning
Paper • 1903.04476 • Published • 1 -
A Survey on Model Compression for Large Language Models
Paper • 2308.07633 • Published • 3 -
A Simple and Effective Pruning Approach for Large Language Models
Paper • 2306.11695 • Published • 3 -
Finding Neurons in a Haystack: Case Studies with Sparse Probing
Paper • 2305.01610 • Published • 2 -
XPrompt: Exploring the Extreme of Prompt Tuning
Paper • 2210.04457 • Published • 1 -
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models
Paper • 2303.10464 • Published • 1 -
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
Paper • 2111.00160 • Published • 1 -
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Paper • 2309.14174 • Published • 1 -
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
Paper • 2211.11315 • Published • 1 -
Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
Paper • 2310.06694 • Published • 3 -
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
Paper • 2310.05015 • Published • 1 -
Can pruning make Large Language Models more efficient?
Paper • 2310.04573 • Published • 1 -
Compressing LLMs: The Truth is Rarely Pure and Never Simple
Paper • 2310.01382 • Published • 1 -
DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models
Paper • 2305.17651 • Published • 1 -
PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations
Paper • 2203.16965 • Published • 1 -
Task-Agnostic Structured Pruning of Speech Representation Models
Paper • 2306.01385 • Published • 1 -
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation
Paper • 2305.11685 • Published • 2 -
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Paper • 2306.03805 • Published • 1 -
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
Paper • 2205.11005 • Published • 1 -
Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
Paper • 2311.04902 • Published • 1 -
Leveraging Structured Pruning of Convolutional Neural Networks
Paper • 2206.06247 • Published • 1 -
You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership
Paper • 2111.00162 • Published • 1 -
Sparse then Prune: Toward Efficient Vision Transformers
Paper • 2307.11988 • Published • 1 -
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning
Paper • 2305.18563 • Published • 1 -
Incremental Task Learning with Incremental Rank Updates
Paper • 2207.09074 • Published • 1 -
On the Soft-Subnetwork for Few-shot Class Incremental Learning
Paper • 2209.07529 • Published • 1 -
Forget-free Continual Learning with Soft-Winning SubNetworks
Paper • 2303.14962 • Published • 2 -
Exclusive Supermask Subnetwork Training for Continual Learning
Paper • 2210.10209 • Published • 1 -
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
Paper • 2305.18444 • Published • 1 -
SparCL: Sparse Continual Learning on the Edge
Paper • 2209.09476 • Published • 1 -
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Paper • 2308.14831 • Published • 1 -
Dynamic Sparse Training with Structured Sparsity
Paper • 2305.02299 • Published • 1 -
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization
Paper • 2308.02060 • Published • 1 -
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Paper • 2211.16667 • Published • 1 -
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive Regularization
Paper • 2308.07163 • Published • 1 -
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
Paper • 2209.14624 • Published • 1 -
End-to-End Neural Network Compression via ell_1{ell_2} Regularized Latency Surrogates
Paper • 2306.05785 • Published • 1 -
Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction
Paper • 2110.08232 • Published • 1 -
LAPP: Layer Adaptive Progressive Pruning for Compressing CNNs from Scratch
Paper • 2309.14157 • Published • 1 -
Weight-dependent Gates for Network Pruning
Paper • 2007.02066 • Published • 1 -
Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning
Paper • 2301.11063 • Published • 1 -
Soft Masking for Cost-Constrained Channel Pruning
Paper • 2211.02206 • Published • 1 -
Group channel pruning and spatial attention distilling for object detection
Paper • 2306.01526 • Published • 1 -
Structured Pruning Learns Compact and Accurate Models
Paper • 2204.00408 • Published • 1 -
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Paper • 2210.15523 • Published • 1 -
Latency Adjustable Transformer Encoder for Language Understanding
Paper • 2201.03327 • Published • 1 -
Learned Token Pruning for Transformers
Paper • 2107.00910 • Published • 1 -
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
Paper • 2010.03688 • Published • 1 -
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Paper • 2305.17328 • Published • 2 -
Pruning Pre-trained Language Models Without Fine-Tuning
Paper • 2210.06210 • Published • 1 -
Frustratingly Simple Memory Efficiency for Pre-trained Language Models via Dynamic Embedding Pruning
Paper • 2309.08708 • Published • 3 -
Are Sixteen Heads Really Better than One?
Paper • 1905.10650 • Published • 2 -
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
Paper • 2207.03677 • Published • 1 -
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints
Paper • 2308.15003 • Published • 1 -
Growing Efficient Deep Networks by Structured Continuous Sparsification
Paper • 2007.15353 • Published • 1 -
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
Paper • 2206.00277 • Published • 1 -
SiRA: Sparse Mixture of Low Rank Adaptation
Paper • 2311.09179 • Published • 9 -
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Paper • 2311.13171 • Published • 1 -
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models
Paper • 2004.12406 • Published • 1 -
Less is More: Selective Layer Finetuning with SubTuning
Paper • 2302.06354 • Published • 1 -
Prune Once for All: Sparse Pre-Trained Language Models
Paper • 2111.05754 • Published • 1 -
To prune, or not to prune: exploring the efficacy of pruning for model compression
Paper • 1710.01878 • Published • 1 -
Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training
Paper • 2302.10798 • Published • 1 -
SortedNet, a Place for Every Network and Every Network in its Place: Towards a Generalized Solution for Training Many-in-One Neural Networks
Paper • 2309.00255 • Published • 1 -
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT)
Paper • 2309.08968 • Published • 23 -
LLM-Pruner: On the Structural Pruning of Large Language Models
Paper • 2305.11627 • Published • 3 -
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Paper • 2309.13192 • Published • 1 -
Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
Paper • 2106.02914 • Published • 1 -
Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy
Paper • 2111.09635 • Published • 1 -
I3D: Transformer architectures with input-dependent dynamic depth for speech recognition
Paper • 2303.07624 • Published • 1 -
An EMO Joint Pruning with Multiple Sub-networks: Fast and Effect
Paper • 2303.16212 • Published • 1 -
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
Paper • 2212.01977 • Published • 1 -
Neural Network Pruning as Spectrum Preserving Process
Paper • 2307.08982 • Published • 1 -
Pruning a neural network using Bayesian inference
Paper • 2308.02451 • Published • 1 -
Class-dependent Compression of Deep Neural Networks
Paper • 1909.10364 • Published • 1 -
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach
Paper • 2302.10483 • Published • 1 -
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Paper • 1909.12778 • Published • 1 -
Emergence of Segmentation with Minimalistic White-Box Transformers
Paper • 2308.16271 • Published • 16 -
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
Paper • 2311.13110 • Published • 2 -
Sparse Probabilistic Circuits via Pruning and Growing
Paper • 2211.12551 • Published • 2 -
Learning to Prune Deep Neural Networks via Reinforcement Learning
Paper • 2007.04756 • Published • 1 -
Pruning Very Deep Neural Network Channels for Efficient Inference
Paper • 2211.08339 • Published • 1 -
Fast Convex Pruning of Deep Neural Networks
Paper • 1806.06457 • Published • 1 -
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Paper • 1912.08881 • Published • 1 -
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
Paper • 2308.10438 • Published • 1 -
Advancing Model Pruning via Bi-level Optimization
Paper • 2210.04092 • Published • 1 -
COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks
Paper • 2212.12770 • Published • 2 -
When Layers Play the Lottery, all Tickets Win at Initialization
Paper • 2301.10835 • Published • 1 -
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability
Paper • 2306.00045 • Published • 1 -
Pruning at Initialization -- A Sketching Perspective
Paper • 2305.17559 • Published • 1 -
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Paper • 2202.02643 • Published • 1 -
Why Random Pruning Is All We Need to Start Sparse
Paper • 2210.02412 • Published • 1 -
Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
Paper • 2110.15343 • Published • 2 -
Adaptive Activation-based Structured Pruning
Paper • 2201.10520 • Published • 1 -
Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation
Paper • 2111.08577 • Published • 1 -
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
Paper • 2304.06941 • Published • 1 -
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Paper • 2308.06767 • Published • 1 -
Pruning Deep Neural Networks from a Sparsity Perspective
Paper • 2302.05601 • Published • 1 -
White-Box Transformers via Sparse Rate Reduction
Paper • 2306.01129 • Published • 1 -
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Paper • 2102.03773 • Published • 1 -
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Paper • 2004.14765 • Published • 1 -
Regularization-based Pruning of Irrelevant Weights in Deep Neural Architectures
Paper • 2204.04977 • Published • 1 -
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization
Paper • 2309.06805 • Published • 1 -
Learning Activation Functions for Sparse Neural Networks
Paper • 2305.10964 • Published • 1 -
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
Paper • 2011.09905 • Published • 1 -
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
Paper • 2209.15176 • Published • 1 -
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model
Paper • 2212.09811 • Published • 1 -
Sparse Low-rank Adaptation of Pre-trained Language Models
Paper • 2311.11696 • Published • 2 -
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach
Paper • 2111.02399 • Published • 1 -
Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning
Paper • 2212.12651 • Published • 1 -
UPSCALE: Unconstrained Channel Pruning
Paper • 2307.08771 • Published • 1 -
PruMUX: Augmenting Data Multiplexing with Model Compression
Paper • 2305.14706 • Published • 1 -
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
Paper • 2305.18513 • Published • 2 -
Network Pruning via Transformable Architecture Search
Paper • 1905.09717 • Published • 1 -
Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity
Paper • 2309.10285 • Published • 1 -
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
Paper • 2304.02721 • Published • 3 -
The LLM Surgeon
Paper • 2312.17244 • Published • 9 -
GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods
Paper • 2210.06384 • Published • 1 -
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Paper • 2312.13558 • Published • 5 -
Sparsified Model Zoo Twins: Investigating Populations of Sparsified Neural Network Models
Paper • 2304.13718 • Published • 1 -
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Paper • 2205.13571 • Published • 1 -
Trained Rank Pruning for Efficient Deep Neural Networks
Paper • 1812.02402 • Published • 1 -
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
Paper • 2004.14566 • Published • 1 -
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks
Paper • 1904.10921 • Published • 1 -
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
Paper • 2304.04947 • Published • 1 -
Training Neural Networks with Fixed Sparse Masks
Paper • 2111.09839 • Published • 1 -
A Neural Scaling Law from Lottery Ticket Ensembling
Paper • 2310.02258 • Published • 1 -
Methods for Pruning Deep Neural Networks
Paper • 2011.00241 • Published • 1 -
On the Existence of Universal Lottery Tickets
Paper • 2111.11146 • Published • 1 -
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
Paper • 2306.12190 • Published • 1 -
Generalization Bounds for Magnitude-Based Pruning via Sparse Matrix Sketching
Paper • 2305.18789 • Published • 1 -
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Paper • 2106.10404 • Published • 1 -
Lottery Jackpots Exist in Pre-trained Models
Paper • 2104.08700 • Published • 1 -
Grokking Tickets: Lottery Tickets Accelerate Grokking
Paper • 2310.19470 • Published • 1 -
SWAMP: Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
Paper • 2305.14852 • Published • 1 -
Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging
Paper • 2306.16788 • Published • 1 -
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Paper • 2206.07918 • Published • 1 -
Randomly Initialized Subnetworks with Iterative Weight Recycling
Paper • 2303.15953 • Published • 1 -
DASS: Differentiable Architecture Search for Sparse neural networks
Paper • 2207.06968 • Published • 1 -
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Paper • 2308.07939 • Published • 1 -
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
Paper • 2304.11834 • Published • 1 -
AP: Selective Activation for De-sparsifying Pruned Neural Networks
Paper • 2212.06145 • Published • 1 -
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask
Paper • 2206.04385 • Published -
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey
Paper • 2205.08099 • Published • 1 -
Structured Pruning is All You Need for Pruning CNNs at Initialization
Paper • 2203.02549 • Published -
In deep reinforcement learning, a pruned network is a good network
Paper • 2402.12479 • Published • 19 -
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Paper • 2402.16880 • Published • 2 -
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models
Paper • 2405.01943 • Published -
Pruning as a Domain-specific LLM Extractor
Paper • 2405.06275 • Published • 1 -
Structural Pruning of Pre-trained Language Models via Neural Architecture Search
Paper • 2405.02267 • Published -
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Paper • 2407.00928 • Published -
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging
Paper • 2406.16330 • Published -
BlockPruner: Fine-grained Pruning for Large Language Models
Paper • 2406.10594 • Published • 1 -
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
Paper • 2405.16057 • Published -
Pruning Large Language Models with Semi-Structural Adaptive Sparse Training
Paper • 2407.20584 • Published -
Greedy Output Approximation: Towards Efficient Structured Pruning for LLMs Without Retraining
Paper • 2407.19126 • Published