Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration | Paper Citation Record

Observation 8c80bc32-8ed6-42e7-b65c-6117236b9e4a · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 1

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Observation 6a50b399-4a23-4ca1-8947-ecb2468ef4fe · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Learning both weights and connections for efficient neural network,

Reference 2

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 3

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This paper cites Global sparse momentum sgd for pruning very deep neural networks,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Global sparse momentum sgd for pruning very deep neural networks,

Reference 4

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Observation 99a7f68d-6783-4555-b17d-4b90287a939b · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 5

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Observation d513e7c7-73f8-4f96-acaf-ae5542a217d1 · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Learning filter pruning criteria for deep convolutional neural networks acceleration,

Reference 6

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Observation 236cc7b9-84eb-4f3a-afc0-88a618ab92b6 · outbound

This paper cites Filter pruning via geometric me- dian for deep convolutional neural networks acceleration,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Filter pruning via geometric me- dian for deep convolutional neural networks acceleration,

Reference 7

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This paper cites Filter pruning by switching to neighboring cnns with good attributes,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Filter pruning by switching to neighboring cnns with good attributes,

Reference 8

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Observation 94f13891-e80d-4e60-b2e4-1d20416f90d5 · outbound

This paper cites Post training 4-bit quantization of con- volutional networks for rapid-deployment,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Post training 4-bit quantization of con- volutional networks for rapid-deployment,

Reference 10

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Observation 7e9ff812-6dea-4aa3-bfd3-ffedc5e3945c · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 11

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Observation d4285451-bf15-42c1-917d-d55264cf5b55 · outbound

This paper cites Model compression via distillation and quantization.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Model compression via distillation and quantization

Reference 12

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Observation 352f36a6-1183-404a-b9a7-b36681d1fa87 · outbound

This paper cites Refine myself by teaching myself: Feature refinement via self-knowledge distillation,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Refine myself by teaching myself: Feature refinement via self-knowledge distillation,

Reference 13

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Observation 28271015-a834-45ab-984b-5ac1c2409d7e · outbound

This paper cites Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification,

Reference 14

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Observation c65debca-951e-47d0-9c27-34ce7163c3c5 · outbound

This paper cites Towards efficient tensor decomposition- based dnn model compression with optimization framework,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Towards efficient tensor decomposition- based dnn model compression with optimization framework,

Reference 15

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This paper cites A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search

Reference 16

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Observation ec9c9e1c-a843-43f9-b405-6fb2aa370c77 · outbound

This paper cites Model compression and hardware acceleration for neural networks: A comprehensive survey,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Model compression and hardware acceleration for neural networks: A comprehensive survey,

Reference 18

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Observation 63dc786a-77f8-464d-8f71-cb27e39e8755 · outbound

This paper cites A survey on efficient convolutional neural networks and hardware acceleration,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration A survey on efficient convolutional neural networks and hardware acceleration,

Reference 19

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Observation c91111d1-1a7d-4665-aa9f-bf439e651d5b · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Pruning Filters for Efficient ConvNets

Reference 20

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Observation 2bbb818d-f76c-444c-aab8-f3380b8b754a · outbound

This paper cites Thinet: A filter level pruning method for deep neu- ral network compression,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Thinet: A filter level pruning method for deep neu- ral network compression,

Reference 21

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Observation 4635f5ed-fc6a-4ac6-a398-5184fb1306e8 · outbound

This paper cites Nisp: Pruning networks using neuron importance score prop- agation,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Nisp: Pruning networks using neuron importance score prop- agation,

Reference 22

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Observation 028c291b-b0db-4e73-82a6-a620720d21e8 · outbound

This paper cites Automated filter pruning based on high- dimensional bayesian optimization,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Automated filter pruning based on high- dimensional bayesian optimization,

Reference 23

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Observation 75393631-0ad4-4b44-8c2b-2641a4804984 · outbound

This paper cites Adaptive cnn filter pruning using global importance metric,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Adaptive cnn filter pruning using global importance metric,

Reference 24

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Observation f9f8a5ef-bcc9-4ae0-83bb-09b5a4b525ac · outbound

This paper cites Filter pruning without damaging networks capacity,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Filter pruning without damaging networks capacity,

Reference 25

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This paper cites Magnitude and similarity based variable rate fil- ter pruning for efficient convolution neural networks,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Magnitude and similarity based variable rate fil- ter pruning for efficient convolution neural networks,

Reference 26

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Observation d16864f3-016b-495e-8a85-3ff18266cf7d · outbound

This paper cites DepGraph: Towards Any Structural Pruning.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration DepGraph: Towards Any Structural Pruning

Reference 27

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This paper cites On the channel pruning using graph convolution network for convolutional neural network acceleration,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration On the channel pruning using graph convolution network for convolutional neural network acceleration,

Reference 28

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Observation 69ca1d06-1013-42cf-93a5-733766a270a9 · outbound

This paper cites Optimiz- ing deep neural networks on intelligent edge accelerators via flexible-rate filter pruning,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Optimiz- ing deep neural networks on intelligent edge accelerators via flexible-rate filter pruning,

Reference 29

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Observation be3ac4ec-754f-4aea-be84-246aff6ec170 · outbound

This paper cites Falf convnets: Fatuous auxiliary loss based filter-pruning for efficient deep cnns,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Falf convnets: Fatuous auxiliary loss based filter-pruning for efficient deep cnns,

Reference 30

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This paper cites Performance-aware approxima- tion of global channel pruning for multitask cnns,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Performance-aware approxima- tion of global channel pruning for multitask cnns,

Reference 31

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This paper cites Pruning neural networks at initialization: Why are we missing the mark?.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Pruning neural networks at initialization: Why are we missing the mark?

Reference 32

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Observation c5dcda04-8317-4750-b5b5-77b79560bef3 · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Linear mode connectivity and the lottery ticket hypothesis,

Reference 33

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Observation 778795c9-89b8-4ced-b9bf-b92c86fa566c · outbound

This paper cites Prune- train: fast neural network training by dynamic sparse model reconfiguration,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Prune- train: fast neural network training by dynamic sparse model reconfiguration,

Reference 34

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Observation 550ffccc-47dc-47b3-b571-01075f003c0c · outbound

This paper cites Oyedotun, D.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Oyedotun, D

Reference 35

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This paper cites Only train once: A one-shot neural network training and pruning framework,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Only train once: A one-shot neural network training and pruning framework,

Reference 36

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Observation 542ce889-bc8d-4e4d-8b42-2cbbf8fc493f · outbound

This paper cites OTOV2: Automatic, Generic, User-Friendly.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration OTOV2: Automatic, Generic, User-Friendly

Reference 37

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Observation 6396bf7b-8f5b-488e-93bd-147466d342bb · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration When to prune? a policy towards early structural pruning,

Reference 38

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Learning multiple layers of features from tiny images,

Reference 39

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Observation 2d6ab385-947a-4a8b-b990-41d015804645 · outbound

This paper cites Imagenet large scale visual recog- nition challenge,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Imagenet large scale visual recog- nition challenge,

Reference 40

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This paper cites Deep residual learning for image recog- nition,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Deep residual learning for image recog- nition,

Reference 41

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Observation db96e12c-b3c2-43ad-8a4f-f00d527bdf55 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 42

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This paper cites Identity mappings in deep residual net- works,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Identity mappings in deep residual net- works,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:00:47.320278Z

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Observation ebbd47a5-c5a4-428a-b932-3037ddef9b90 · outbound

This paper cites Wide Residual Networks.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Wide Residual Networks

Reference 44

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This paper cites Automatic differentiation in pytorch,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Automatic differentiation in pytorch,

Reference 45

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Observation a6014b34-8911-43d3-ad96-0c910fda7cec · outbound

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

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Hrank: Filter pruning using high-rank feature map,

Reference 46

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Observation 5e5ed434-2b59-4c42-82bf-75bb343cd340 · outbound

This paper cites Network pruning via performance maximization,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Network pruning via performance maximization,

Reference 47

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Observation 41169b26-ed1d-47eb-96d6-31b101872ed1 · outbound

This paper cites Rethinking the Value of Network Pruning.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Rethinking the Value of Network Pruning

Reference 48

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This paper cites Fusion-catalyzed pruning for optimizing deep learning on intelligent edge devices,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Fusion-catalyzed pruning for optimizing deep learning on intelligent edge devices,

Reference 49

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Observation 3d5c01b3-db4b-402b-bb5e-8a9d2bc1d6d6 · outbound

This paper cites Leveraging filter correlations for deep model compression,.

Loss-Aware Automatic Selection of Structured Pruning Criteria for Deep Neural Network Acceleration Leveraging filter correlations for deep model compression,

Reference 50

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