SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning | Paper Citation Record

Observation 5c7eab1f-0a52-417b-a132-fe2bb2f6f666 · outbound

This paper cites Spiking neural networks hardware implementations and challenges: A survey.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spiking neural networks hardware implementations and challenges: A survey

Reference 1

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Observation 8120bea2-0290-4a44-85d5-15e59f7e1fd2 · outbound

This paper cites Differentiable hierarchical and surrogate gradient search for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Differentiable hierarchical and surrogate gradient search for spiking neural networks

Reference 2

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Observation 62cf9a60-1653-4110-92ea-9e27868f0451 · outbound

This paper cites Pruning of deep spiking neural networks through gradient rewiring.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Pruning of deep spiking neural networks through gradient rewiring

Reference 3

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Observation b933431d-1414-4a37-80f7-61f15de6c530 · outbound

This paper cites State tran- sition of dendritic spines improves learning of sparse spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning State tran- sition of dendritic spines improves learning of sparse spiking neural networks

Reference 4

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Observation e726e5c7-7782-454a-a4a9-ca63be87175c · outbound

This paper cites A Unified Framework for Soft Threshold Pruning.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning A Unified Framework for Soft Threshold Pruning

Reference 5

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Observation 78a418f2-9046-49e1-b0e5-2c04645c99fd · outbound

This paper cites Spatio-temporal pruning and quantization for low-latency spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spatio-temporal pruning and quantization for low-latency spiking neural networks

Reference 6

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Observation e431b43b-f1ce-4929-84fc-95204499d3e0 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Loihi: A neuromorphic manycore processor with on-chip learning

Reference 7

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Observation fbbb5e4f-c5b2-497f-a90a-8de1bd6773bc · outbound

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Imagenet: A large- scale hierarchical image database

Reference 8

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Observation 3ce725f6-36a1-4173-acd6-49ce53c6665b · outbound

This paper cites Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Reference 9

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Observation b7cfd2d1-f966-45df-8d3b-67dca8fa0437 · outbound

This paper cites Ec-snn: splitting deep spiking neural networks for edge devices.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Ec-snn: splitting deep spiking neural networks for edge devices

Reference 10

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Observation d255538b-e28a-4f8e-813a-e33b0e0acd83 · outbound

This paper cites Deep residual learning in spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Deep residual learning in spiking neural networks

Reference 11

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Observation fb07b622-9c37-4d97-a1e1-40688fed8031 · outbound

This paper cites Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence

Reference 12

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Observation 5e536f2b-0cde-4d42-a1ad-d39d1a7c32e2 · outbound

This paper cites Large-scale neuromorphic computing systems.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Large-scale neuromorphic computing systems

Reference 13

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Observation 7ca1b261-2948-48ab-a078-e1e79fe4ff14 · outbound

This paper cites Jointly training and pruning cnns via learnable agent guidance and alignment.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Jointly training and pruning cnns via learnable agent guidance and alignment

Reference 14

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This paper cites A low effort approach to structured cnn design using pca.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning A low effort approach to structured cnn design using pca

Reference 15

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Observation bf83ab6d-5de2-432b-9b45-40b7fa9aea3c · outbound

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning A survey on efficient convolutional neural networks and hardware acceleration

Reference 16

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Observation c6fdafb3-4ac9-4d42-b613-ff2b3d7b21c0 · outbound

This paper cites Model compression using progressive channel pruning.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Model compression using progressive channel pruning

Reference 17

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Observation 832a954c-cf88-4681-8300-131ebcfd63c1 · outbound

This paper cites Im-loss: information maximization loss for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Im-loss: information maximization loss for spiking neural networks

Reference 18

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Observation e78f94a3-bf2f-4a49-a54a-333387a97f9b · outbound

This paper cites Rmp-loss: Regularizing membrane potential distribution for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Rmp-loss: Regularizing membrane potential distribution for spiking neural networks

Reference 19

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Observation 9b917aac-82cd-44ff-8413-d1c446e494f8 · outbound

This paper cites Adaptive sparse structure development with pruning and regeneration for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Adaptive sparse structure development with pruning and regeneration for spiking neural networks

Reference 20

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Observation c758e88e-f1af-4eb8-b742-7bcad6ce404a · outbound

This paper cites A progressive training framework for spiking neural networks with learnable multi-hierarchical model.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning A progressive training framework for spiking neural networks with learnable multi-hierarchical model

Reference 21

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Observation a729aa5c-db64-41db-a4e5-1b1d12ed0c84 · outbound

This paper cites Deep residual learning for image recognition.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Deep residual learning for image recognition

Reference 22

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Observation 0750170b-baa6-4478-b53e-c9c8da943d05 · outbound

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Structured pruning for deep convolutional neural networks: A survey

Reference 23

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Amc: Automl for model compression and acceleration on mobile devices

Reference 24

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This paper cites 1.1 computing’s energy problem (and what we can do about it).

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning 1.1 computing’s energy problem (and what we can do about it)

Reference 25

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This paper cites Spiking deep residual networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spiking deep residual networks

Reference 26

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This paper cites CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

Reference 27

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This paper cites Neural architecture search for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Neural architecture search for spiking neural networks

Reference 28

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Learning multiple layers of features from tiny images

Reference 29

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This paper cites Tiny imagenet visual recognition challenge.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Tiny imagenet visual recognition challenge

Reference 30

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This paper cites Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spike2Former: Efficient Spiking Transformer for High-performance Image Segmentation

Reference 31

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This paper cites Pruning Filters for Efficient ConvNets.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Pruning Filters for Efficient ConvNets

Reference 32

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This paper cites Cifar10-dvs: an event- stream dataset for object classification.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Cifar10-dvs: an event- stream dataset for object classification

Reference 33

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This paper cites Efficient structure slimming for spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Efficient structure slimming for spiking neural networks

Reference 34

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This paper cites Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning

Reference 35

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This paper cites Continuous control with deep reinforcement learning.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Continuous control with deep reinforcement learning

Reference 36

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This paper cites DARTS: Differentiable Architecture Search.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning DARTS: Differentiable Architecture Search

Reference 37

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This paper cites LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

Reference 38

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This paper cites A survey on evolutionary neural architecture search.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning A survey on evolutionary neural architecture search

Reference 39

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Observation be9f8dde-928c-4d9f-b37f-45c60f1c3aa2 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Learning efficient convolutional networks through network slimming

Reference 40

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This paper cites Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection

Reference 41

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This paper cites Autosnn: Towards energy-efficient spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Autosnn: Towards energy-efficient spiking neural networks

Reference 42

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This paper cites Towards artificial general intelligence with hybrid tianjic chip architecture.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Towards artificial general intelligence with hybrid tianjic chip architecture

Reference 43

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This paper cites Efficient 3D Recognition with Event-driven Spike Sparse Convolution.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Efficient 3D Recognition with Event-driven Spike Sparse Convolution

Reference 44

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This paper cites Gated attention coding for training high-performance and efficient spiking neural networks.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Gated attention coding for training high-performance and efficient spiking neural networks

Reference 45

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning On the distribution of the correlation coefficient in small samples

Reference 46

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This paper cites Wang, Chiao Liu, and Kaushik Roy.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Wang, Chiao Liu, and Kaushik Roy

Reference 47

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Observation b03f2f02-88f6-462b-9e14-14407c33c50e · outbound

This paper cites Towards energy efficient spiking neural networks: An unstructured pruning framework.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Towards energy efficient spiking neural networks: An unstructured pruning framework

Reference 48

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Observation f5ae19d5-af80-49bd-964c-aacedbdbaba9 · outbound

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

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Very deep convolutional networks for large-scale image recognition

Reference 49

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Observation 45a8f93e-7607-4735-aafd-27343df14cf1 · outbound

This paper cites Snn-bert: Training-efficient spiking neural networks for energy-efficient bert.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Snn-bert: Training-efficient spiking neural networks for energy-efficient bert

Reference 50

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Observation f9412ea4-dfad-4026-94e1-3c5b6d4b6dbb · outbound

This paper cites RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration

Reference 51

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Observation 98a7cb0c-a6df-4790-b3e2-4a031690c7cf · outbound

This paper cites Efficient spiking neural network design via neural architecture search.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Efficient spiking neural network design via neural architecture search

Reference 52

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Temporal-wise attention spiking neural networks for event streams classification

Reference 53

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Attention spiking neural networks

Reference 54

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Spike- driven transformer

Reference 55

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Scaling spike-driven transformer with efficient spike firing approximation training

Reference 56

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Glif: A unified gated leaky integrate-and- fire neuron for spiking neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:52.151713Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Workload-balanced pruning for sparse spiking neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:52.012861Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Going deeper with directly-trained larger spiking neural networks

Reference 59

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods

Reference 60

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning It can be found in 4 for method and 5.2 for experiments results

Reference 63

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Limitations

Reference 64

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not include theoretical results

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.576672Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning And we have also described our algorithm in 4 with details

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.389841Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Additionally, we have uploaded our code

Reference 67

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 68

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning 4, both of which demonstrated significant statistical relevance/significance

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.068543Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not include experiments

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:50.839880Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Unresolved cited work

Reference 71

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This paper cites This work promises to bring significant advantages to Edge AI as introduced in Sec 1.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning This work promises to bring significant advantages to Edge AI as introduced in Sec 1

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:50.249784Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper poses no such risks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:50.108085Z

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Reference 74

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not release new assets

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:49.659232Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 76

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:49.417619Z

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SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning Answer: [NA] Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:49.182947Z

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Observation 37a2d7e2-9ad5-4f37-bdfe-54c906a147c9 · outbound

This paper cites doi: 10.24963/ijcai.2021/236.

SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning doi: 10.24963/ijcai.2021/236

Reference 1721

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Reference 2014

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