Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study | Paper Citation Record

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study SliceGPT: Compress Large Language Models by Deleting Rows and Columns

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study BinaryBERT: Pushing the Limit of BERT Quantization

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study int8 (): 8-bit matrix multiplicationfortransformersatscale.AdvancesinNeuralInformationProcessing Systems 35, 30318–30332 (2022)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Reducing Transformer Depth on Demand with Structured Dropout

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

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This paper cites International Journal of Computer Vision 129(6), 1789–1819 (Mar Pruning and Recovery Techniques for Compressing MLLMs 15 2021).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study International Journal of Computer Vision 129(6), 1789–1819 (Mar Pruning and Recovery Techniques for Compressing MLLMs 15 2021)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MiniLLM: On-Policy Distillation of Large Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Efficient Multimodal Learning from Data-centric Perspective

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Distilling the Knowledge in a Neural Network

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study The Curious Case of Neural Text Degeneration

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study LoRA: Low-Rank Adaptation of Large Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Microsoft Research Blog (2023)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Master’s thesis, University of Washington (2024)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study TinyBERT: Distilling BERT for Natural Language Understanding

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Signal Propagation Perspective for Pruning Neural Networks at Initialization

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Pruning Filters for Efficient ConvNets

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Evaluating Object Hallucination in Large Vision-Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MixKD: Towards Efficient Distillation of Large-scale Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Visual Instruction Tuning

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Group Fisher Pruning for Practical Network Compression

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Advances in Neural Information Processing Systems 35, 2507–2521 (2022)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Advances in neural information processing systems36, 21702–21720 (2023)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Structured Pruning of a BERT-based Question Answering Model

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Unresolved cited work

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: International conference on machine learning

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Computer Speech & Language77, 101429 (2023)

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Movement Pruning: Adaptive Sparsity by Fine-Tuning

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Patient Knowledge Distillation for BERT Model Compression

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study LLaMA: Open and Efficient Foundation Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Survey on Knowledge Distillation of Large Language Models

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

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This paper cites A Survey on Multimodal Large Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Survey on Multimodal Large Language Models

Reference 57

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This paper cites Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

Reference 58

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This paper cites In: Proceedings of CVPR (2024).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of CVPR (2024)

Reference 59

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This paper cites In: 2019 Fifth Workshop on Energy Efficient Machine Learn- ing and Cognitive Computing - NeurIPS Edition (EMC2-NIPS).

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: 2019 Fifth Workshop on Energy Efficient Machine Learn- ing and Cognitive Computing - NeurIPS Edition (EMC2-NIPS)

Reference 60

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This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:22:49.965938Z

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This paper cites Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models

Reference 62

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This paper cites In: Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study In: Proceedings of the 1st International Workshop on Efficient Multimedia Computing under Limited

Reference 63

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