CoNNect: Connectivity-Based Regularization for Structural Pruning | Paper Citation Record

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This paper cites Structured pruning of deep convolutional neural networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Structured pruning of deep convolutional neural networks

Reference 1

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Observation d80d3733-052f-43d8-8a32-c296a64e498b · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

CoNNect: Connectivity-Based Regularization for Structural Pruning Piqa: Reasoning about physical commonsense in natural language

Reference 2

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

CoNNect: Connectivity-Based Regularization for Structural Pruning A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations

Reference 3

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This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

CoNNect: Connectivity-Based Regularization for Structural Pruning Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 4

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

CoNNect: Connectivity-Based Regularization for Structural Pruning Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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This paper cites Neural network training using l_1 -regularization and bi-fidelity data.

CoNNect: Connectivity-Based Regularization for Structural Pruning Neural network training using l_1 -regularization and bi-fidelity data

Reference 6

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This paper cites Depgraph: Towards any structural pruning.

CoNNect: Connectivity-Based Regularization for Structural Pruning Depgraph: Towards any structural pruning

Reference 7

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This paper cites MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

CoNNect: Connectivity-Based Regularization for Structural Pruning MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 8

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Observation 275145ca-c285-439b-9618-fee1b87008ba · outbound

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

CoNNect: Connectivity-Based Regularization for Structural Pruning The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 9

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Observation 78b96fcc-7a38-486a-9218-91a2f84511d2 · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

CoNNect: Connectivity-Based Regularization for Structural Pruning SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 10

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This paper cites The State of Sparsity in Deep Neural Networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning The State of Sparsity in Deep Neural Networks

Reference 11

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

CoNNect: Connectivity-Based Regularization for Structural Pruning A framework for few-shot language model evaluation

Reference 12

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This paper cites Removal of hidden units and weights for back propagation networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Removal of hidden units and weights for back propagation networks

Reference 13

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Observation e219da10-5308-41a8-9c99-5e24ccf29bb5 · outbound

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

CoNNect: Connectivity-Based Regularization for Structural Pruning Learning both weights and connections for efficient neural network

Reference 14

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This paper cites Optimal brain surgeon and general network pruning.

CoNNect: Connectivity-Based Regularization for Structural Pruning Optimal brain surgeon and general network pruning

Reference 15

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This paper cites Deep residual learning for image recognition.

CoNNect: Connectivity-Based Regularization for Structural Pruning Deep residual learning for image recognition

Reference 16

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Observation e91cc51a-a684-4515-a64f-970390e48275 · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey.

CoNNect: Connectivity-Based Regularization for Structural Pruning Structured pruning for deep convolutional neural networks: A survey

Reference 17

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This paper cites Channel pruning for accelerating very deep neural networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Channel pruning for accelerating very deep neural networks

Reference 18

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Observation cf23aed0-750a-49ce-adba-0c5d4b22b258 · outbound

This paper cites A practical guide to training restricted boltzmann machines.

CoNNect: Connectivity-Based Regularization for Structural Pruning A practical guide to training restricted boltzmann machines

Reference 19

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Observation 88a33e83-09d7-4bb7-8beb-2168f49fb8e8 · outbound

This paper cites Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks

Reference 20

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

CoNNect: Connectivity-Based Regularization for Structural Pruning LoRA: Low-Rank Adaptation of Large Language Models

Reference 21

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Observation 453ff38c-23a5-4e29-9d6b-ff87781a2161 · outbound

This paper cites Data-Driven Sparse Structure Selection for Deep Neural Networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Data-Driven Sparse Structure Selection for Deep Neural Networks

Reference 22

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This paper cites Top-kast: Top-k always sparse training.

CoNNect: Connectivity-Based Regularization for Structural Pruning Top-kast: Top-k always sparse training

Reference 23

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This paper cites A new status index derived from sociometric analysis.

CoNNect: Connectivity-Based Regularization for Structural Pruning A new status index derived from sociometric analysis

Reference 24

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Observation c8b30c3f-d476-4f21-8a15-f8fda48eb7e9 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Semi-Supervised Classification with Graph Convolutional Networks

Reference 25

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This paper cites Learning multiple layers of features from tiny images.

CoNNect: Connectivity-Based Regularization for Structural Pruning Learning multiple layers of features from tiny images

Reference 26

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This paper cites Optimal brain damage.

CoNNect: Connectivity-Based Regularization for Structural Pruning Optimal brain damage

Reference 27

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This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

CoNNect: Connectivity-Based Regularization for Structural Pruning SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 28

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This paper cites A pruning feedforward small-world neural network based on katz centrality for nonlinear system modeling.

CoNNect: Connectivity-Based Regularization for Structural Pruning A pruning feedforward small-world neural network based on katz centrality for nonlinear system modeling

Reference 29

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Observation 16323de5-d540-467b-857a-1e0b313e463d · outbound

This paper cites Decoupled Weight Decay Regularization.

CoNNect: Connectivity-Based Regularization for Structural Pruning Decoupled Weight Decay Regularization

Reference 30

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This paper cites Llm-pruner: On the structural pruning of large language models.

CoNNect: Connectivity-Based Regularization for Structural Pruning Llm-pruner: On the structural pruning of large language models

Reference 31

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This paper cites Building a large annotated corpus of english: The penn treebank.

CoNNect: Connectivity-Based Regularization for Structural Pruning Building a large annotated corpus of english: The penn treebank

Reference 32

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CoNNect: Connectivity-Based Regularization for Structural Pruning Pointer sentinel mixture models

Reference 33

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This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

CoNNect: Connectivity-Based Regularization for Structural Pruning Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 34

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This paper cites Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

CoNNect: Connectivity-Based Regularization for Structural Pruning Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Reference 35

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This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

CoNNect: Connectivity-Based Regularization for Structural Pruning Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 36

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CoNNect: Connectivity-Based Regularization for Structural Pruning Path-sgd: Path-normalized optimization in deep neural networks

Reference 37

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This paper cites Carbon Emissions and Large Neural Network Training.

CoNNect: Connectivity-Based Regularization for Structural Pruning Carbon Emissions and Large Neural Network Training

Reference 38

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This paper cites An analysis of the regularization between l2 and dropout in single hidden layer neural network.

CoNNect: Connectivity-Based Regularization for Structural Pruning An analysis of the regularization between l2 and dropout in single hidden layer neural network

Reference 39

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

CoNNect: Connectivity-Based Regularization for Structural Pruning Winogrande: An adversarial winograd schema challenge at scale

Reference 40

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This paper cites Movement pruning: Adaptive sparsity by fine-tuning.

CoNNect: Connectivity-Based Regularization for Structural Pruning Movement pruning: Adaptive sparsity by fine-tuning

Reference 41

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This paper cites Collective classification in network data.

CoNNect: Connectivity-Based Regularization for Structural Pruning Collective classification in network data

Reference 42

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This paper cites Very deep convolutional networks for large-scale image recognition.

CoNNect: Connectivity-Based Regularization for Structural Pruning Very deep convolutional networks for large-scale image recognition

Reference 43

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

CoNNect: Connectivity-Based Regularization for Structural Pruning A Simple and Effective Pruning Approach for Large Language Models

Reference 44

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CoNNect: Connectivity-Based Regularization for Structural Pruning Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 45

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This paper cites Stanford alpaca: An instruction-following llama model, 2023.

CoNNect: Connectivity-Based Regularization for Structural Pruning Stanford alpaca: An instruction-following llama model, 2023

Reference 46

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This paper cites Evaluating pruning methods.

CoNNect: Connectivity-Based Regularization for Structural Pruning Evaluating pruning methods

Reference 47

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This paper cites Regression shrinkage and selection via the lasso.

CoNNect: Connectivity-Based Regularization for Structural Pruning Regression shrinkage and selection via the lasso

Reference 48

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This paper cites Open and efficient foundation language models.

CoNNect: Connectivity-Based Regularization for Structural Pruning Open and efficient foundation language models

Reference 49

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This paper cites Connectivity matters: Neural network pruning through the lens of effective sparsity.

CoNNect: Connectivity-Based Regularization for Structural Pruning Connectivity matters: Neural network pruning through the lens of effective sparsity

Reference 50

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This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

CoNNect: Connectivity-Based Regularization for Structural Pruning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 51

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This paper cites Learning structured sparsity in deep neural networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Learning structured sparsity in deep neural networks

Reference 52

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This paper cites Structured pruning of convolutional neural networks via l1 regularization.

CoNNect: Connectivity-Based Regularization for Structural Pruning Structured pruning of convolutional neural networks via l1 regularization

Reference 53

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This paper cites Model selection and estimation in regression with grouped variables.

CoNNect: Connectivity-Based Regularization for Structural Pruning Model selection and estimation in regression with grouped variables

Reference 54

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

CoNNect: Connectivity-Based Regularization for Structural Pruning HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 55

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This paper cites Subset-based training and pruning of sigmoid neural networks.

CoNNect: Connectivity-Based Regularization for Structural Pruning Subset-based training and pruning of sigmoid neural networks

Reference 56

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This paper cites Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books.

CoNNect: Connectivity-Based Regularization for Structural Pruning Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Reference 57

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This paper cites Neuron-level structured pruning using polarization regularizer.

CoNNect: Connectivity-Based Regularization for Structural Pruning Neuron-level structured pruning using polarization regularizer

Reference 58

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Observation 74e7369d-ed90-47ed-b99d-21e88bcadc40 · outbound

This paper cites spred: Solving l1 penalty with sgd.

CoNNect: Connectivity-Based Regularization for Structural Pruning spred: Solving l1 penalty with sgd

Reference 59

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