Scalable iterative pruning of large language and vision models using block coordinate descent | Paper Citation Record

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This paper cites No pruning.

Scalable iterative pruning of large language and vision models using block coordinate descent No pruning

Reference 1

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This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Scalable iterative pruning of large language and vision models using block coordinate descent Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 2

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This paper cites Scaling Laws for Neural Language Models.

Scalable iterative pruning of large language and vision models using block coordinate descent Scaling Laws for Neural Language Models

Reference 3

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This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations.

Scalable iterative pruning of large language and vision models using block coordinate descent A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations

Reference 4

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This paper cites Block Pruning For Faster Transformers.

Scalable iterative pruning of large language and vision models using block coordinate descent Block Pruning For Faster Transformers

Reference 5

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This paper cites The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models.

Scalable iterative pruning of large language and vision models using block coordinate descent The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

Reference 6

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This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Scalable iterative pruning of large language and vision models using block coordinate descent The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 7

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This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Scalable iterative pruning of large language and vision models using block coordinate descent A Simple and Effective Pruning Approach for Large Language Models

Reference 8

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This paper cites SparseGPT: Massive language models can be accurately pruned in one-shot.

Scalable iterative pruning of large language and vision models using block coordinate descent SparseGPT: Massive language models can be accurately pruned in one-shot

Reference 9

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This paper cites A user’s guide to tabu search.

Scalable iterative pruning of large language and vision models using block coordinate descent A user’s guide to tabu search

Reference 10

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This paper cites Mistral 7B.

Scalable iterative pruning of large language and vision models using block coordinate descent Mistral 7B

Reference 11

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This paper cites Training data-efficient image transformers & distillation through attention.

Scalable iterative pruning of large language and vision models using block coordinate descent Training data-efficient image transformers & distillation through attention

Reference 12

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This paper cites Optimal brain damage.Advances in neural information processing systems, 2, 1989.

Scalable iterative pruning of large language and vision models using block coordinate descent Optimal brain damage.Advances in neural information processing systems, 2, 1989

Reference 13

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This paper cites Optimal brain surgeon and general network pruning.

Scalable iterative pruning of large language and vision models using block coordinate descent Optimal brain surgeon and general network pruning

Reference 14

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This paper cites Thecombinatorialbrainsurgeon: Pruning weights that cancel one another in neural networks.

Scalable iterative pruning of large language and vision models using block coordinate descent Thecombinatorialbrainsurgeon: Pruning weights that cancel one another in neural networks

Reference 15

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This paper cites Learning both weights and connections for efficient neural network.

Scalable iterative pruning of large language and vision models using block coordinate descent Learning both weights and connections for efficient neural network

Reference 16

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This paper cites What is the state of neural network pruning? Proceedings of machine learning and systems, 2:129–146, 2020.

Scalable iterative pruning of large language and vision models using block coordinate descent What is the state of neural network pruning? Proceedings of machine learning and systems, 2:129–146, 2020

Reference 17

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This paper cites Woodfisher: Efficient second-order approximation for neural network compression.

Scalable iterative pruning of large language and vision models using block coordinate descent Woodfisher: Efficient second-order approximation for neural network compression

Reference 18

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This paper cites Fast as CHITA: Neural network pruning with combinatorial optimization.

Scalable iterative pruning of large language and vision models using block coordinate descent Fast as CHITA: Neural network pruning with combinatorial optimization

Reference 19

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This paper cites Building an iterative heuristic solver for a quantum annealer.

Scalable iterative pruning of large language and vision models using block coordinate descent Building an iterative heuristic solver for a quantum annealer

Reference 20

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This paper cites From local to global ground states in Ising spin glasses.

Scalable iterative pruning of large language and vision models using block coordinate descent From local to global ground states in Ising spin glasses

Reference 21

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This paper cites Partitioning optimization problems for hybrid classical.

Scalable iterative pruning of large language and vision models using block coordinate descent Partitioning optimization problems for hybrid classical

Reference 22

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This paper cites The Llama 3 Herd of Models.

Scalable iterative pruning of large language and vision models using block coordinate descent The Llama 3 Herd of Models

Reference 23

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This paper cites A Quantum Approximate Optimization Algorithm.

Scalable iterative pruning of large language and vision models using block coordinate descent A Quantum Approximate Optimization Algorithm

Reference 24

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This paper cites Quantum approximate optimization with hard and soft constraints.

Scalable iterative pruning of large language and vision models using block coordinate descent Quantum approximate optimization with hard and soft constraints

Reference 25

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This paper cites Q-CHOP: Quantum constrained hamiltonian optimization.

Scalable iterative pruning of large language and vision models using block coordinate descent Q-CHOP: Quantum constrained hamiltonian optimization

Reference 26

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This paper cites Optimal brain surgeon: Extensions and performance comparisons.

Scalable iterative pruning of large language and vision models using block coordinate descent Optimal brain surgeon: Extensions and performance comparisons

Reference 27

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This paper cites Extremal optimization.

Scalable iterative pruning of large language and vision models using block coordinate descent Extremal optimization

Reference 28

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This paper cites Solving QUBOs with a quantum-amenable branch and bound method.

Scalable iterative pruning of large language and vision models using block coordinate descent Solving QUBOs with a quantum-amenable branch and bound method

Reference 29

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This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 2017.

Scalable iterative pruning of large language and vision models using block coordinate descent Attention is all you need.Advances in Neural Information Processing Systems, 2017

Reference 30

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This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Scalable iterative pruning of large language and vision models using block coordinate descent BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 31

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This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Scalable iterative pruning of large language and vision models using block coordinate descent GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 32

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This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Scalable iterative pruning of large language and vision models using block coordinate descent HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 33

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This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Scalable iterative pruning of large language and vision models using block coordinate descent Winogrande: An adversarial winograd schema challenge at scale

Reference 34

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Scalable iterative pruning of large language and vision models using block coordinate descent Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 35

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This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Scalable iterative pruning of large language and vision models using block coordinate descent Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 36

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This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Scalable iterative pruning of large language and vision models using block coordinate descent Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 37

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This paper cites Imagenet: A large-scale hierarchical image database.

Scalable iterative pruning of large language and vision models using block coordinate descent Imagenet: A large-scale hierarchical image database

Reference 38

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research , 21(140):1–67, 2020.

Scalable iterative pruning of large language and vision models using block coordinate descent Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research , 21(140):1–67, 2020

Reference 39

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This paper cites A framework for few-shot language model evaluation, 12 2023.

Scalable iterative pruning of large language and vision models using block coordinate descent A framework for few-shot language model evaluation, 12 2023

Reference 40

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This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

Scalable iterative pruning of large language and vision models using block coordinate descent Tune: A Research Platform for Distributed Model Selection and Training

Reference 41

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This paper cites Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures.

Scalable iterative pruning of large language and vision models using block coordinate descent Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures

Reference 42

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Scalable iterative pruning of large language and vision models using block coordinate descent Pytorch: An imperative style, high-performance deep learning library

Reference 43

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This paper cites Maxwell’s demon at work: Efficient pruning by leveraging saturation of neurons.

Scalable iterative pruning of large language and vision models using block coordinate descent Maxwell’s demon at work: Efficient pruning by leveraging saturation of neurons

Reference 44

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Observation 1fb82e9a-66c8-4cea-9f1d-2942a901592f · outbound

This paper cites Pruning neural network models for gene regulatory dynamics using data and domain knowledge.

Scalable iterative pruning of large language and vision models using block coordinate descent Pruning neural network models for gene regulatory dynamics using data and domain knowledge

Reference 45

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Observation 18907e00-39f2-45b5-98d7-b702538ce200 · outbound

This paper cites Quantum neural network compression.

Scalable iterative pruning of large language and vision models using block coordinate descent Quantum neural network compression

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:00:00.974818Z

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Observation e71ab982-a2c8-4e28-9126-6ea806b1d8c4 · outbound

This paper cites QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits.

Scalable iterative pruning of large language and vision models using block coordinate descent QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits

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This paper cites gradient accumulation.

Scalable iterative pruning of large language and vision models using block coordinate descent gradient accumulation

Reference 48

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