Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks | Paper Citation Record

Observation 6c3c5e3f-26da-41bc-af8a-f8e6c31f9f16 · outbound

This paper cites Optimal brain damage,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Optimal brain damage,

Reference 1

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

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Learning both weights and connections for efficient neural network,

Reference 2

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This paper cites Learning sparse neural networks through ℓ0 regularization,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Learning sparse neural networks through ℓ0 regularization,

Reference 3

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 4

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This paper cites Designing energy-efficient convolu- tional neural networks using energy-aware pruning,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Designing energy-efficient convolu- tional neural networks using energy-aware pruning,

Reference 5

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Observation 86b81fe1-3010-4fb6-94d3-b6583da3d8e1 · outbound

This paper cites Pruning convolutional neural networks for resource efficient inference,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Pruning convolutional neural networks for resource efficient inference,

Reference 6

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Observation 72fd4b82-a371-4a54-b7e5-565e1862935d · outbound

This paper cites What is the state of neural network pruning?.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks What is the state of neural network pruning?

Reference 7

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This paper cites To prune, or not to prune: Exploring the efficacy of pruning for model compression,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks To prune, or not to prune: Exploring the efficacy of pruning for model compression,

Reference 8

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Observation a37dbd5e-46d1-4116-9f54-6a2c00609bc7 · outbound

This paper cites ProxSGD: Training structured neural networks under regularization and constraints,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks ProxSGD: Training structured neural networks under regularization and constraints,

Reference 9

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This paper cites Adaptive proximal gradient methods for structured neural networks,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Adaptive proximal gradient methods for structured neural networks,

Reference 10

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This paper cites Linear mode connectivity and the lottery ticket hypothesis,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Linear mode connectivity and the lottery ticket hypothesis,

Reference 11

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Observation 29931445-88ad-417b-bc7c-d342c2a33ede · outbound

This paper cites Winning the lottery with continuous sparsification,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Winning the lottery with continuous sparsification,

Reference 12

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Observation a01b871b-65f6-4dfe-b5c2-d953a2030499 · outbound

This paper cites Learning efficient convolutional networks through network slimming,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Learning efficient convolutional networks through network slimming,

Reference 13

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Observation 7d9ac10d-e71a-4920-bcd8-78c088b1b250 · outbound

This paper cites Rethinking the value of network pruning,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Rethinking the value of network pruning,

Reference 14

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Observation 25be4a0e-5dd1-47f8-8f21-e3ebebb7d6f9 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks SeReNe: Sensitivity-based regularization of neurons for structured sparsity in neural networks,

Reference 15

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This paper cites EDropout: Energy-based dropout and pruning of deep neural networks,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks EDropout: Energy-based dropout and pruning of deep neural networks,

Reference 16

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Observation ee82f8e9-a8a9-4e2f-b203-a20f9d70062c · outbound

This paper cites DSA: More efficient budgeted pruning via differentiable sparsity allocation,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks DSA: More efficient budgeted pruning via differentiable sparsity allocation,

Reference 17

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Observation 847a6313-e15d-423e-bf40-8b5e0b107ff5 · outbound

This paper cites HRank: Filter pruning using high-rank feature map,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks HRank: Filter pruning using high-rank feature map,

Reference 18

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Observation 3bf4a069-ae50-404d-9c20-815f9522f37d · outbound

This paper cites Network pruning using adaptive exemplar filters,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Network pruning using adaptive exemplar filters,

Reference 19

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Observation 13bb67dc-d66c-4c8b-9e8f-6504f5ea718b · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Filter sketch for network pruning,

Reference 20

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Observation f6a4f539-a5b5-4d05-95a4-023610c64a98 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Carrying out CNN channel pruning in a white box,

Reference 21

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This paper cites Regression shrinkage and selection via the Lasso,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Regression shrinkage and selection via the Lasso,

Reference 22

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Observation c326d906-179e-46fb-a5f7-a1cd1f285d16 · outbound

This paper cites Enhancing sparsity by reweighted ℓ1 minimization,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Enhancing sparsity by reweighted ℓ1 minimization,

Reference 23

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Observation aba1b2b6-dac2-4946-9746-312a93414051 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Compressed sensing recovery via nonconvex shrinkage penalties,

Reference 24

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Observation 7ef91d4a-c7ee-47b6-90ef-71a5fa4eb810 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Nearly unbiased variable selection under minimax concave penalty,

Reference 25

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Sparsest solutions of underdetermined linear systems via ℓq minimization for 0 < q≤ 1,

Reference 26

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Sparse regularization via convex analysis,

Reference 27

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Observation 9980682a-922d-44f6-a556-de9a4fd290b9 · outbound

This paper cites Sparse learning with concave regularization: relaxation of the irrepresentable condition,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Sparse learning with concave regularization: relaxation of the irrepresentable condition,

Reference 28

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This paper cites A survey on nonconvex regularization-based sparse and low-rank recovery in signal processing, statistics, and machine learning,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks A survey on nonconvex regularization-based sparse and low-rank recovery in signal processing, statistics, and machine learning,

Reference 29

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks On the convergence of the iterative shrinkage/thresholding algorithm with a weakly convex penalty,

Reference 30

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Observation 8ecd2127-535d-4341-beaa-342e71cf52d7 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks A general iterative shrinkage and thresholding algorithm for non-convex regularized opti- mization problems,

Reference 31

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Observation 10510c37-163c-42c1-a133-b0535d973a53 · outbound

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Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Minimization of non-smooth, non-convex functionals by iterative thresholding,

Reference 32

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Observation 76d4fb7c-e2d6-442c-a799-abba55c42d28 · outbound

This paper cites Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems,

Reference 33

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Observation ef172209-39ad-4821-a51b-fc1a5ded65a7 · outbound

This paper cites What’s hidden in a randomly weighted neural network?.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks What’s hidden in a randomly weighted neural network?

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:25.084977Z

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Observation 087610bd-7bb1-426c-bab1-3979ecd64f5d · outbound

This paper cites Proving the lottery ticket hypothesis: Pruning is all you need,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Proving the lottery ticket hypothesis: Pruning is all you need,

Reference 35

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Observation 5d7e4fcd-3402-4471-8205-69b3c6ecb333 · outbound

This paper cites The MNIST Database of Handwritten Digit Images for Machine Learning Research,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks The MNIST Database of Handwritten Digit Images for Machine Learning Research,

Reference 36

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Observation 840b6588-9db5-4e39-b876-8262459c92d9 · outbound

This paper cites Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:25.021652Z

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Observation 91522b1a-28b1-4b1b-90a0-3d4f508f6fa6 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Pruning Filters for Efficient ConvNets

Reference 38

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Observation b27f0fff-65f8-43be-86db-b4a5c5c70b70 · outbound

This paper cites Nisp: Pruning networks using neuron importance score propagation,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Nisp: Pruning networks using neuron importance score propagation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:25.001578Z

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Observation 4e7970e2-cccf-498c-8507-69158628cfc1 · outbound

This paper cites Discrimination-aware channel pruning for deep neural networks,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Discrimination-aware channel pruning for deep neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.979811Z

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Observation fcc14f41-bfea-4e36-8fba-756c1dddfebd · outbound

This paper cites Scop: Scientific control for reliable neural network pruning,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Scop: Scientific control for reliable neural network pruning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.954972Z

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Observation 9f3fdb0b-d493-4ad7-893e-6855cb77abcc · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 42

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Observation abfe45a9-5e03-43a0-beb0-d78f7a73700d · outbound

This paper cites Towards optimal structured cnn pruning via generative adversarial learning,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Towards optimal structured cnn pruning via generative adversarial learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.930500Z

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Observation 0583a40f-ccee-44da-93a1-3aaa3d7c9d39 · outbound

This paper cites Filter pruning via geometric median for deep convolutional neural networks acceleration,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Filter pruning via geometric median for deep convolutional neural networks acceleration,

Reference 44

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Observation 388e6ab6-438b-49b0-97c7-a77b2ebf2347 · outbound

This paper cites Amc: Automl for model compression and acceleration on mobile devices,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Amc: Automl for model compression and acceleration on mobile devices,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.891607Z

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Observation b566968f-dbc9-4f72-9f47-ac846a078221 · outbound

This paper cites Operation-aware soft channel pruning using differentiable masks,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Operation-aware soft channel pruning using differentiable masks,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.870892Z

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Observation 1a656a52-d238-4c6d-90c8-92195105af6e · outbound

This paper cites Learning filter pruning criteria for deep convolutional neural networks acceleration,.

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks Learning filter pruning criteria for deep convolutional neural networks acceleration,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:41:24.853431Z

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