Advancing Weight and Channel Sparsification with Enhanced Saliency | Paper Citation Record

Observation 949fc57a-9b67-4328-91c6-a695a7340b40 · outbound

This paper cites Learning the number of neurons in deep networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Learning the number of neurons in deep networks

Reference 1

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This paper cites Constraint-aware deep neural network compression.

Advancing Weight and Channel Sparsification with Enhanced Saliency Constraint-aware deep neural network compression

Reference 2

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This paper cites cuDNN: Efficient Primitives for Deep Learning.

Advancing Weight and Channel Sparsification with Enhanced Saliency cuDNN: Efficient Primitives for Deep Learning

Reference 3

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This paper cites Towards efficient model compression via learned global ranking.

Advancing Weight and Channel Sparsification with Enhanced Saliency Towards efficient model compression via learned global ranking

Reference 4

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This paper cites Nest: A neural network synthesis tool based on a grow-and- prune paradigm.

Advancing Weight and Channel Sparsification with Enhanced Saliency Nest: A neural network synthesis tool based on a grow-and- prune paradigm

Reference 5

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This paper cites Progressive skeletonization: Trimming more fat from a network at initialization.

Advancing Weight and Channel Sparsification with Enhanced Saliency Progressive skeletonization: Trimming more fat from a network at initialization

Reference 6

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Imagenet: A large-scale hierarchical image database

Reference 7

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This paper cites Sparse Networks from Scratch: Faster Training without Losing Performance.

Advancing Weight and Channel Sparsification with Enhanced Saliency Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 8

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This paper cites Approximated oracle filter pruning for destructive cnn width optimization.

Advancing Weight and Channel Sparsification with Enhanced Saliency Approximated oracle filter pruning for destructive cnn width optimization

Reference 9

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Observation b8aecdd4-3ffd-485f-9d95-257768bbc1da · outbound

This paper cites Network pruning via transformable architecture search.

Advancing Weight and Channel Sparsification with Enhanced Saliency Network pruning via transformable architecture search

Reference 10

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Observation 3381f6d9-6416-40ad-baa5-bba18d293bff · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Advancing Weight and Channel Sparsification with Enhanced Saliency An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

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This paper cites Rigging the lottery: Making all tickets winners.

Advancing Weight and Channel Sparsification with Enhanced Saliency Rigging the lottery: Making all tickets winners

Reference 12

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This paper cites The pascal visual object classes (voc) challenge.

Advancing Weight and Channel Sparsification with Enhanced Saliency The pascal visual object classes (voc) challenge

Reference 13

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Observation 3745d38d-55a3-4f90-94c4-7cb8d8c3b6aa · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency The State of Sparsity in Deep Neural Networks

Reference 14

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Observation 537b6141-9abb-4f4f-a854-7e6cd412bc87 · outbound

This paper cites Dmcp: Differentiable markov channel pruning for neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dmcp: Differentiable markov channel pruning for neural networks

Reference 15

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This paper cites Eie: Efficient inference engine on compressed deep neural network.

Advancing Weight and Channel Sparsification with Enhanced Saliency Eie: Efficient inference engine on compressed deep neural network

Reference 16

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This paper cites Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding.

Advancing Weight and Channel Sparsification with Enhanced Saliency Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding

Reference 17

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This paper cites Second order derivatives for network pruning: Optimal brain surgeon.

Advancing Weight and Channel Sparsification with Enhanced Saliency Second order derivatives for network pruning: Optimal brain surgeon

Reference 18

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This paper cites Deep residual learning for image recognition.

Advancing Weight and Channel Sparsification with Enhanced Saliency Deep residual learning for image recognition

Reference 19

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Learning filter pruning criteria for deep convolutional neural networks acceleration

Reference 20

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Observation 631bd3a5-2685-49ae-9766-2669dcfadbee · outbound

This paper cites Soft filter pruning for accelerating deep convolutional neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Soft filter pruning for accelerating deep convolutional neural networks

Reference 21

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Observation 53554526-04e4-4ca8-a4d9-d20f947a1be7 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Amc: Automl for model compression and acceleration on mobile devices

Reference 22

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Observation 8fa2adab-9740-40e9-a529-c17deb31ca48 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Filter pruning via geometric median for deep convolutional neural networks acceleration

Reference 23

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This paper cites Chex: Channel exploration for cnn model compression.

Advancing Weight and Channel Sparsification with Enhanced Saliency Chex: Channel exploration for cnn model compression

Reference 24

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Observation 74c6abe2-51d0-4263-8b1b-e1157ce6c8c5 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Advancing Weight and Channel Sparsification with Enhanced Saliency MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 25

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Observation bcec9907-1c88-4fb7-83f9-b946d4a0c885 · outbound

This paper cites Speed/accuracy trade-offs for modern convolutional object detectors.

Advancing Weight and Channel Sparsification with Enhanced Saliency Speed/accuracy trade-offs for modern convolutional object detectors

Reference 26

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Observation ff9fff44-19c0-4c75-99d3-32e79fe5033b · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Top-kast: Top- k always sparse training

Reference 27

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Observation 5e8ab5fe-6387-4e7d-993f-01ecc6b47135 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Operation-aware soft channel pruning using differentiable masks

Reference 28

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Observation be87bb64-8dc1-4dbd-8b13-02902baad362 · outbound

This paper cites Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights

Reference 29

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Learning multiple layers of features from tiny images

Reference 30

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This paper cites Soft threshold weight reparameterization for learnable sparsity.

Advancing Weight and Channel Sparsification with Enhanced Saliency Soft threshold weight reparameterization for learnable sparsity

Reference 31

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This paper cites Dynamic Sparse Training with Structured Sparsity.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dynamic Sparse Training with Structured Sparsity

Reference 32

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This paper cites Optimal brain damage.

Advancing Weight and Channel Sparsification with Enhanced Saliency Optimal brain damage

Reference 33

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This paper cites Snip: Single-shot network pruning based on connection sensitivity.

Advancing Weight and Channel Sparsification with Enhanced Saliency Snip: Single-shot network pruning based on connection sensitivity

Reference 34

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This paper cites Eagleeye: Fast sub-net evaluation for efficient neural network pruning.

Advancing Weight and Channel Sparsification with Enhanced Saliency Eagleeye: Fast sub-net evaluation for efficient neural network pruning

Reference 35

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This paper cites Dynamic slimmable network.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dynamic slimmable network

Reference 36

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This paper cites Pruning filters for efficient convnets.

Advancing Weight and Channel Sparsification with Enhanced Saliency Pruning filters for efficient convnets

Reference 37

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Hrank: Filter pruning using high-rank feature map

Reference 38

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This paper cites Accelerating convolutional networks via global & dynamic filter pruning.

Advancing Weight and Channel Sparsification with Enhanced Saliency Accelerating convolutional networks via global & dynamic filter pruning

Reference 39

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This paper cites Dynamic model pruning with feedback.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dynamic model pruning with feedback

Reference 40

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This paper cites Sparse training via boosting pruning plasticity with neuroregeneration.

Advancing Weight and Channel Sparsification with Enhanced Saliency Sparse training via boosting pruning plasticity with neuroregeneration

Reference 41

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This paper cites Do we actually need dense over- parameterization? in-time over-parameterization in sparse training.

Advancing Weight and Channel Sparsification with Enhanced Saliency Do we actually need dense over- parameterization? in-time over-parameterization in sparse training

Reference 42

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This paper cites Ssd: Single shot multibox detector.

Advancing Weight and Channel Sparsification with Enhanced Saliency Ssd: Single shot multibox detector

Reference 43

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This paper cites Metapruning: Meta learning for automatic neural network channel pruning.

Advancing Weight and Channel Sparsification with Enhanced Saliency Metapruning: Meta learning for automatic neural network channel pruning

Reference 44

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This paper cites Optimistic initialization for exploration in continuous control.

Advancing Weight and Channel Sparsification with Enhanced Saliency Optimistic initialization for exploration in continuous control

Reference 45

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Advancing Weight and Channel Sparsification with Enhanced Saliency SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 46

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This paper cites Learning sparse neural networks through l_0 regularization.

Advancing Weight and Channel Sparsification with Enhanced Saliency Learning sparse neural networks through l_0 regularization

Reference 47

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This paper cites Prunetrain: fast neural network training by dynamic sparse model reconfiguration.

Advancing Weight and Channel Sparsification with Enhanced Saliency Prunetrain: fast neural network training by dynamic sparse model reconfiguration

Reference 48

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Observation 59a8aceb-4316-4e0b-9655-c96850aa9606 · outbound

This paper cites Effective model sparsification by scheduled grow-and-prune methods.

Advancing Weight and Channel Sparsification with Enhanced Saliency Effective model sparsification by scheduled grow-and-prune methods

Reference 49

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This paper cites Domain-independent optimistic initialization for reinforcement learning.

Advancing Weight and Channel Sparsification with Enhanced Saliency Domain-independent optimistic initialization for reinforcement learning

Reference 50

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This paper cites Are sixteen heads really better than one? NeurIPS, 32:14014–14024, 2019.

Advancing Weight and Channel Sparsification with Enhanced Saliency Are sixteen heads really better than one? NeurIPS, 32:14014–14024, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.750168Z

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This paper cites Accelerating Sparse Deep Neural Networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Accelerating Sparse Deep Neural Networks

Reference 52

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.742560Z

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This paper cites Variational dropout sparsifies deep neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Variational dropout sparsifies deep neural networks

Reference 54

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This paper cites Importance estimation for neural network pruning.

Advancing Weight and Channel Sparsification with Enhanced Saliency Importance estimation for neural network pruning

Reference 55

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Observation 278db66c-a55b-4700-a4eb-952a88cf40ca · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 56

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Observation a6fdf60a-1c6f-4b38-a849-51cecdd09de1 · outbound

This paper cites Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization.

Advancing Weight and Channel Sparsification with Enhanced Saliency Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.721067Z

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Observation f8c26bc1-bd23-4894-b023-35fa57b7a5f6 · outbound

This paper cites Exploring sparsity in recurrent neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Exploring sparsity in recurrent neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.713091Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91f389d4-1ea6-4656-a45e-066076c3b021 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Dsa: More efficient budgeted pruning via differentiable sparsity allocation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.705671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ac3ce014-036d-4d91-95ce-63bac7949ed1 · outbound

This paper cites an unresolved cited work.

Advancing Weight and Channel Sparsification with Enhanced Saliency Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:15:29.697963Z

Source-reported events for the cited work

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Observation 6804ecab-7b6d-4f97-b8dd-a1a9c2d3007e · outbound

This paper cites Automatic differentiation in pytorch.

Advancing Weight and Channel Sparsification with Enhanced Saliency Automatic differentiation in pytorch

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.689872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 57948e7c-3274-4fb8-896a-708a91b50907 · outbound

This paper cites Imagenet large scale visual recognition challenge.

Advancing Weight and Channel Sparsification with Enhanced Saliency Imagenet large scale visual recognition challenge

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.682492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8e3498fe-ddec-4a33-9fc4-872a0d635c68 · outbound

This paper cites Hardware-aware latency pruning for real-time 3d object detection.

Advancing Weight and Channel Sparsification with Enhanced Saliency Hardware-aware latency pruning for real-time 3d object detection

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.674866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0c3f04ea-e02d-470e-8703-bb16be1c8468 · outbound

This paper cites When to prune? a policy towards early structural pruning.

Advancing Weight and Channel Sparsification with Enhanced Saliency When to prune? a policy towards early structural pruning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.667221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d484646f-02ed-432e-a779-5d9f9af3f4a7 · outbound

This paper cites Structural pruning via latency-saliency knapsack.

Advancing Weight and Channel Sparsification with Enhanced Saliency Structural pruning via latency-saliency knapsack

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.659423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d24a1030-3dfd-46e3-8313-3cb1bfdebb7d · outbound

This paper cites Training sparse neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Training sparse neural networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.651221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80941dd7-7f3d-4e84-a28e-833e27721dfa · outbound

This paper cites Sparse connection and pruning in large dynamic artificial neural networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Sparse connection and pruning in large dynamic artificial neural networks

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.642228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d7872565-4aea-4832-b23c-393c655a46df · outbound

This paper cites Pruning for Better Domain Generalizability.

Advancing Weight and Channel Sparsification with Enhanced Saliency Pruning for Better Domain Generalizability

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:15:29.372429Z

Source-reported events for the cited work

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Observation 43b892b4-e353-4e60-961f-552cd1a86466 · outbound

This paper cites Refining Pre-Trained Motion Models.

Advancing Weight and Channel Sparsification with Enhanced Saliency Refining Pre-Trained Motion Models

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:15:29.361829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42585bf1-22bf-4280-9067-b994e630e938 · outbound

This paper cites Disparse: Disentangled sparsification for multitask model compression.

Advancing Weight and Channel Sparsification with Enhanced Saliency Disparse: Disentangled sparsification for multitask model compression

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.633810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9ff3d07-da0e-4f34-aa70-e749a71dc77e · outbound

This paper cites Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint.

Advancing Weight and Channel Sparsification with Enhanced Saliency Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:15:29.350310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9e10052-1036-4757-9e9a-a33bf7b6690b · outbound

This paper cites Revisiting deformable convolution for depth completion.

Advancing Weight and Channel Sparsification with Enhanced Saliency Revisiting deformable convolution for depth completion

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.625352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 828e3eb8-a3dc-43f3-8b18-2b07a3681292 · outbound

This paper cites Towards better structured pruning saliency by reorganizing convolution.

Advancing Weight and Channel Sparsification with Enhanced Saliency Towards better structured pruning saliency by reorganizing convolution

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.617816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5dd02a86-36c3-4b6c-a022-ebf205144f89 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Scop: Scientific control for reliable neural network pruning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.610165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9005ee7-7234-4141-84a3-2d8f9d702b39 · outbound

This paper cites Evaluating pruning methods.

Advancing Weight and Channel Sparsification with Enhanced Saliency Evaluating pruning methods

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.602056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 613d38ac-b2f3-4f64-9d76-20931ae9e4d0 · outbound

This paper cites Neural pruning via growing regularization.

Advancing Weight and Channel Sparsification with Enhanced Saliency Neural pruning via growing regularization

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.594513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bee0078b-a465-45e5-bce0-9c3f0b9c283a · outbound

This paper cites Interspace pruning: Using adaptive filter representations to improve training of sparse cnns.

Advancing Weight and Channel Sparsification with Enhanced Saliency Interspace pruning: Using adaptive filter representations to improve training of sparse cnns

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.586904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 83d099cf-cf2b-4f20-8449-81427a90975b · outbound

This paper cites Discovering neural wirings.

Advancing Weight and Channel Sparsification with Enhanced Saliency Discovering neural wirings

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.579274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1430b109-3f31-40ba-ab59-38cb4e1655ad · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Advancing Weight and Channel Sparsification with Enhanced Saliency Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.571971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation df4abc63-96b5-4d29-a726-31f0323d29b1 · outbound

This paper cites Netadapt: Platform-aware neural network adaptation for mobile applications.

Advancing Weight and Channel Sparsification with Enhanced Saliency Netadapt: Platform-aware neural network adaptation for mobile applications

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.564242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e32b15ad-dbff-4ba4-9e94-4f16fd55d19f · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.556864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5061deb2-4a1d-4654-884a-d0285b9b8bc2 · outbound

This paper cites Autoslim: Towards one-shot architecture search for channel numbers.

Advancing Weight and Channel Sparsification with Enhanced Saliency Autoslim: Towards one-shot architecture search for channel numbers

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.548659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c0dab5df-7c57-4034-b4f9-a75e6851bb75 · outbound

This paper cites Layer freezing & data sieving: Missing pieces of a generic framework for sparse training.

Advancing Weight and Channel Sparsification with Enhanced Saliency Layer freezing & data sieving: Missing pieces of a generic framework for sparse training

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.541099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 27b71317-97a1-4ce8-9898-28245f46d5c3 · outbound

This paper cites Mest: Accurate and fast memory-economic sparse training framework on the edge.

Advancing Weight and Channel Sparsification with Enhanced Saliency Mest: Accurate and fast memory-economic sparse training framework on the edge

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.533020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c490ae0-65a2-4677-97bb-0936b81c42bd · outbound

This paper cites Growing efficient deep networks by structured continuous sparsification.

Advancing Weight and Channel Sparsification with Enhanced Saliency Growing efficient deep networks by structured continuous sparsification

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.524284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5c645400-a474-41bb-91ca-07dc4e0de600 · outbound

This paper cites Wide residual networks.

Advancing Weight and Channel Sparsification with Enhanced Saliency Wide residual networks

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.516188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b3818bd2-fb1c-4006-b3ee-eae93d057b6a · outbound

This paper cites Learning n: m fine-grained structured sparse neural networks from scratch.

Advancing Weight and Channel Sparsification with Enhanced Saliency Learning n: m fine-grained structured sparse neural networks from scratch

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.508054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3797b626-99b3-48ec-96ce-b1c14f5ac909 · outbound

This paper cites Efficient neural network training via forward and backward propagation sparsification.

Advancing Weight and Channel Sparsification with Enhanced Saliency Efficient neural network training via forward and backward propagation sparsification

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.500182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee4fdd32-38ec-492c-ae8b-3d95ca6b56c6 · outbound

This paper cites Effective sparsification of neural networks with global sparsity constraint.

Advancing Weight and Channel Sparsification with Enhanced Saliency Effective sparsification of neural networks with global sparsity constraint

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.492236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af2407b5-6890-4770-bbf0-46450cb6dde0 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-09T04:15:29.297781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f45e3d0e-e6dd-49a3-9c1a-4bc08d440318 · outbound

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

Advancing Weight and Channel Sparsification with Enhanced Saliency Neuron-level structured pruning using polarization regularizer

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.483241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c498629c-09da-4dde-8cf8-15ca91ac3d5c · outbound

This paper cites prior" importance information and performing “posterior.

Advancing Weight and Channel Sparsification with Enhanced Saliency prior" importance information and performing “posterior

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.474617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f04cbe53-b438-4999-8817-e2d2a3b0d6b7 · outbound

This paper cites WithN : M sparsity, we sparsify N neurons out of M contiguous neurons.

Advancing Weight and Channel Sparsification with Enhanced Saliency WithN : M sparsity, we sparsify N neurons out of M contiguous neurons

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.466328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 262e52b3-0cd3-491c-bd22-576b4d3ccc55 · outbound

This paper cites FLOPs needed for a single forward pass inference of sparse model is computed by counting the total number of multiplications and additions.

Advancing Weight and Channel Sparsification with Enhanced Saliency FLOPs needed for a single forward pass inference of sparse model is computed by counting the total number of multiplications and additions

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.457976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cea108a3-0e7e-4384-a2ee-84b6c935c877 · outbound

This paper cites update budget.

Advancing Weight and Channel Sparsification with Enhanced Saliency update budget

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.449574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7264dae2-c7d4-46ff-9904-80fd1fb72ac0 · outbound

This paper cites We run all experiments on ImageNet and PASCAL VOC with eight NVIDIA Tesla V100 GPUs.

Advancing Weight and Channel Sparsification with Enhanced Saliency We run all experiments on ImageNet and PASCAL VOC with eight NVIDIA Tesla V100 GPUs

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T04:15:29.441057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 76a11504-e304-49cd-930a-e5782e07c150 · outbound

This paper cites 2, 5, 6, 15.

Advancing Weight and Channel Sparsification with Enhanced Saliency 2, 5, 6, 15

Reference 255

Resolution: parse uncertain; no resolver link, observed 2026-08-09T04:15:29.066054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers