Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping | Paper Citation Record

Observation 01b05448-adae-47ca-8b2c-6078bfafae7d · outbound

This paper cites An introduction to probabilistic spiking neural networks: Probabilistic models, learning rules, and applications,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping An introduction to probabilistic spiking neural networks: Probabilistic models, learning rules, and applications,

Reference 1

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Observation 43f7366c-20de-4e07-9100-5d5d6c1e0b87 · outbound

This paper cites A low-cost and high-speed hardware implementation of spiking neural network,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A low-cost and high-speed hardware implementation of spiking neural network,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:10.211450Z

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Observation 886975dc-c420-4fbc-9755-bf7a25482cfc · outbound

This paper cites A hybrid neural coding approach for pattern recognition with spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A hybrid neural coding approach for pattern recognition with spiking neural networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.964688Z

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Observation 249843fd-0e97-4983-815f-6ae2d99f82b8 · outbound

This paper cites Spikeprop: backprop- agation for networks of spiking neurons.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikeprop: backprop- agation for networks of spiking neurons

Reference 4

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Observation fc317037-d569-40b0-bf28-2abeea47332e · outbound

This paper cites Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based opti- mization to spiking neural networks,

Reference 5

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Observation e6697c8e-64db-42b8-a6b4-21b940612c3f · outbound

This paper cites Scaling spike-driven transformer with efficient spike firing approximation training,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Scaling spike-driven transformer with efficient spike firing approximation training,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.659659Z

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Observation b9871b61-927f-4eec-80a2-efd4c806d90a · outbound

This paper cites Graph spiking attention network: Sparsity, efficiency and robustness,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Graph spiking attention network: Sparsity, efficiency and robustness,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.506992Z

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Observation 8e6c129e-17ed-4674-9274-b79e1d5f774c · outbound

This paper cites Fast- classifying, high-accuracy spiking deep networks through weight and threshold balancing,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Fast- classifying, high-accuracy spiking deep networks through weight and threshold balancing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.314657Z

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Observation 0995bb4d-9445-4d2d-b2df-eed2e0585d9b · outbound

This paper cites Spiking deep convolutional neural networks for energy-efficient object recognition,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spiking deep convolutional neural networks for energy-efficient object recognition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.166296Z

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Observation e9415372-ab10-4b98-a05c-0d8aed353142 · outbound

This paper cites Fast-snn: Fast spiking neural network by converting quantized ann,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Fast-snn: Fast spiking neural network by converting quantized ann,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:09.058525Z

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Observation 6aed8564-6b22-425f-b258-161971e1e72e · outbound

This paper cites Spikeconverter: An efficient conversion framework zipping the gap between artificial neural networks and spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikeconverter: An efficient conversion framework zipping the gap between artificial neural networks and spiking neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.913230Z

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Observation 80ae3c06-db2c-4dbc-866e-75e4eaca7871 · outbound

This paper cites An efficient brain- inspired accelerator using a high-accuracy conversion algorithm for spiking deformable CNN,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping An efficient brain- inspired accelerator using a high-accuracy conversion algorithm for spiking deformable CNN,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.764766Z

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Observation 2ac27fdc-1728-41dd-ba7f-9a75f9ab2eb9 · outbound

This paper cites Toward energy-efficient spike-based deep reinforcement learning with temporal coding,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Toward energy-efficient spike-based deep reinforcement learning with temporal coding,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.626585Z

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Observation 9dd96ab0-3011-4d28-b788-03a49d47e17d · outbound

This paper cites Spikezip-tf: Conversion is all you need for transformer-based SNN,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikezip-tf: Conversion is all you need for transformer-based SNN,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.503943Z

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Observation ec14d785-fe61-4523-8784-4c2499afc482 · outbound

This paper cites Optimal ANN- SNN conversion for high-accuracy and ultra-low-latency spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Optimal ANN- SNN conversion for high-accuracy and ultra-low-latency spiking neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.302557Z

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Observation 976ad41e-7ada-463d-b0f6-201d212372c5 · outbound

This paper cites Cs- qcfs: Bridging the performance gap in ultra-low latency spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Cs- qcfs: Bridging the performance gap in ultra-low latency spiking neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:08.117094Z

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Observation 7d7d9b93-edd8-4b20-a672-99d85f9572f0 · outbound

This paper cites Quantization framework for fast spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Quantization framework for fast spiking neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.970300Z

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Observation 6c01a8fe-84a9-40d7-8023-b7ff7df2e861 · outbound

This paper cites Optimal conversion of conventional artificial neural networks to spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Optimal conversion of conventional artificial neural networks to spiking neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.815861Z

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Observation 947eb1ef-a735-4a68-b002-e5945150f2d2 · outbound

This paper cites A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.643527Z

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Observation a94295a3-f4f9-4ed9-8b10-aa7dfec05aa8 · outbound

This paper cites Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration

Reference 20

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Observation 1d3d5bed-3399-4a68-9dcd-30daa2015e9a · outbound

This paper cites Efficient learning with augmented spikes: A case study with image classification,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Efficient learning with augmented spikes: A case study with image classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.472250Z

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Observation 76ed91f4-3b2b-4545-8b6e-7d68ae661ea3 · outbound

This paper cites Efficient converted spiking neural network for 3d and 2d classification,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Efficient converted spiking neural network for 3d and 2d classification,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.316805Z

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Observation f70225a1-b932-411c-a116-1230a129e063 · outbound

This paper cites Lm-ht SNN: Enhancing the performance of SNN to ANN counterpart through learnable multi- hierarchical threshold model,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Lm-ht SNN: Enhancing the performance of SNN to ANN counterpart through learnable multi- hierarchical threshold model,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:07.139386Z

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Observation 511d2449-c897-4eff-b998-e1c5de0f0516 · outbound

This paper cites Synaptic learning with augmented spikes,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Synaptic learning with augmented spikes,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.957611Z

Source-reported events for the cited work

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Observation 71169f42-c077-4bcd-b20e-180f98bf3102 · outbound

This paper cites Construct- ing accurate and efficient deep spiking neural networks with double- threshold and augmented schemes,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Construct- ing accurate and efficient deep spiking neural networks with double- threshold and augmented schemes,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.805771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dfa10d3c-3270-4638-a136-aa2c52f9f4e1 · outbound

This paper cites Augmapping: Accurate and efficient inference with deep double-threshold spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Augmapping: Accurate and efficient inference with deep double-threshold spiking neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.615027Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9282e1af-bff3-4127-a4d2-d782aecf3434 · outbound

This paper cites Con- version of continuous-valued deep networks to efficient event-driven networks for image classification,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Con- version of continuous-valued deep networks to efficient event-driven networks for image classification,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.413826Z

Source-reported events for the cited work

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Observation a7644860-4800-4bc6-88f8-fc3c6e82f718 · outbound

This paper cites Optimized potential initialization for low-latency spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Optimized potential initialization for low-latency spiking neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.223876Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 76400cf5-c2b7-4107-8d77-d21113a5c154 · outbound

This paper cites Reducing ANN-SNN conversion error through residual membrane potential,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Reducing ANN-SNN conversion error through residual membrane potential,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:06.088164Z

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Observation a52b4b66-ba87-428b-955f-f717ecb17d3b · outbound

This paper cites Bridging the gap between ANNs and SNNs by calibrating offset spikes,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Bridging the gap between ANNs and SNNs by calibrating offset spikes,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.927756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4f9d7f29-b97b-4cac-8eb3-4c98649b2205 · outbound

This paper cites Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Optimal ANN-SNN conversion for fast and accurate inference in deep spiking neural networks,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.722797Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1aa5ce57-e371-4d69-843c-f357623b24b5 · outbound

This paper cites A new ANN- SNN conversion method with high accuracy, low latency and good robustness,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A new ANN- SNN conversion method with high accuracy, low latency and good robustness,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.563132Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 301702f7-2e75-478c-8dc7-4b4a5cbe8942 · outbound

This paper cites Symmetric-threshold ReLU for fast and nearly lossless ANN-SNN conversion,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Symmetric-threshold ReLU for fast and nearly lossless ANN-SNN conversion,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.429375Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd218f7c-2544-4919-9768-1b3e3c9a6d7b · outbound

This paper cites A unified optimization framework of ANN-SNN conversion: Towards optimal mapping from activation values to firing rates,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A unified optimization framework of ANN-SNN conversion: Towards optimal mapping from activation values to firing rates,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.271955Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e8943ca-c341-48ed-9f5d-66fb5729a389 · outbound

This paper cites Masked spiking transformer,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Masked spiking transformer,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:05.079737Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4aa4a7c-c68c-4232-b2f0-22f9812a8162 · outbound

This paper cites Spikedattention: Training-free and fully spike-driven transformer-to-SNN conversion with winner-oriented spike shift for softmax operation,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikedattention: Training-free and fully spike-driven transformer-to-SNN conversion with winner-oriented spike shift for softmax operation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:04.845332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f107b00b-3a60-43c8-b93d-f5fb70f65a44 · outbound

This paper cites Rmp-SNN: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Rmp-SNN: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:04.621911Z

Source-reported events for the cited work

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Observation 775d2b7f-1cf1-4b65-bfa0-b1ba36b6c555 · outbound

This paper cites Efficient ANN-SNN conversion with error compensation learning,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Efficient ANN-SNN conversion with error compensation learning,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:04.455257Z

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Observation 9fbb8e95-ff5c-4bb4-b98c-1e148822edc3 · outbound

This paper cites Signed neuron with memory: Towards simple, accurate and high-efficient ANN-SNN conversion,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Signed neuron with memory: Towards simple, accurate and high-efficient ANN-SNN conversion,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:04.263004Z

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Observation 76aa5363-cfa0-418f-ae33-4a0a06f9ee61 · outbound

This paper cites A universal ANN- to-SNN framework for achieving high accuracy and low latency deep spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A universal ANN- to-SNN framework for achieving high accuracy and low latency deep spiking neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:04.065762Z

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Observation 3c88701b-de4b-4a95-b001-095b4693595d · outbound

This paper cites Ternary spike: Learning ternary spikes for spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Ternary spike: Learning ternary spikes for spiking neural networks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:03.932998Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a19160f6-3f00-43bf-91c8-8e1c0b9a3e96 · outbound

This paper cites Rethinking spikes in spiking neural networks for performance enhancement,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Rethinking spikes in spiking neural networks for performance enhancement,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:03.670482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 97fa55b7-63a6-49f7-a10a-4bb3190a04c0 · outbound

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

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Learning multiple layers of features from tiny images,

Reference 43

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Observation 67364de3-55e2-4661-9477-317ce239fc5a · outbound

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

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Imagenet: A large-scale hierarchical image database,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:55.227455Z

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Observation 4b7c2a94-8249-4432-adf1-9c9e46d5d7c5 · outbound

This paper cites CIFAR10-DVS: An event-stream dataset for object classification,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping CIFAR10-DVS: An event-stream dataset for object classification,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:03.489319Z

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Observation 94bb27bb-5224-4e2c-967b-69bfd93ff43f · outbound

This paper cites Caltech 101,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Caltech 101,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:03.233940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ecb887e6-3ef0-4128-9718-49cd8d98c7b4 · outbound

This paper cites Tactilesgnet: A spiking graph neural network for event-based tactile object recognition,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Tactilesgnet: A spiking graph neural network for event-based tactile object recognition,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:03.042012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3460c5ca-3f5b-40b0-ac55-f6d57eca0c2b · outbound

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

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Rmp-loss: Regularizing membrane potential distribution for spiking neural networks,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:02.846689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ddf12b82-3cdf-4d5b-aadc-c1fe1caa437b · outbound

This paper cites Low latency conversion of artificial neural network models to rate-encoded spiking neural net- works,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Low latency conversion of artificial neural network models to rate-encoded spiking neural net- works,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:02.664843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c55c056-2ada-4f41-a1bc-ad1d40dceb90 · outbound

This paper cites Training spiking neural networks with local tandem learning,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Training spiking neural networks with local tandem learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:02.441888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7c12e0aa-e087-44ff-9304-fbfb3c7266ed · outbound

This paper cites Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Converting High-Performance and Low-Latency SNNs through Explicit Modelling of Residual Error in ANNs

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:39:58.116780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c97c193-fd5b-4dbb-8575-5091a74a7acd · outbound

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

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:55.961661Z

Source-reported events for the cited work

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Observation 783cabcd-777e-4e47-8c02-00c43336c27f · outbound

This paper cites Deep residual learning for image recognition,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Deep residual learning for image recognition,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:56.086725Z

Source-reported events for the cited work

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Observation 1305ea87-43e6-4682-83eb-9b464bd97fb7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:56.176499Z

Source-reported events for the cited work

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Observation 6c12d2fd-2b24-413f-845d-4d4e3b6b09c4 · outbound

This paper cites Spikformer: When spiking neural network meets transformer,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikformer: When spiking neural network meets transformer,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:02.243324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fea6e049-aa31-4d97-8153-50ca63956e98 · outbound

This paper cites Spike- driven transformer,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spike- driven transformer,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:02.060962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c545fd4-adcb-47ca-866f-3c3164902506 · outbound

This paper cites Going deeper with directly-trained larger spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Going deeper with directly-trained larger spiking neural networks,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:56.498706Z

Source-reported events for the cited work

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Observation 25343ffb-643c-46b5-9063-970c305704a3 · outbound

This paper cites Sglformer: Spiking global-local-fusion transformer with high performance,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Sglformer: Spiking global-local-fusion transformer with high performance,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:01.832304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b64d737-f764-4e28-87bd-84ecf6ad9488 · outbound

This paper cites One-step spiking trans- former with a linear complexity,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping One-step spiking trans- former with a linear complexity,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:01.642782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 837155db-ab4b-4b59-931f-8a39b89bed91 · outbound

This paper cites Assisting training of deep spiking neural networks with parameter initialization,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Assisting training of deep spiking neural networks with parameter initialization,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:01.357629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0683e912-8405-4b2c-9102-e94939242925 · outbound

This paper cites Spikingresformer: Bridging resnet and vision transformer in spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spikingresformer: Bridging resnet and vision transformer in spiking neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:00.943634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 98250531-8f82-4065-a8e1-aad388a91860 · outbound

This paper cites QKFormer: Hierarchical spiking transformer using q-k attention,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping QKFormer: Hierarchical spiking transformer using q-k attention,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:00.708533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4243ae37-b723-438d-9782-572f8f84b3e1 · outbound

This paper cites Spiking transformer with spatial-temporal attention,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Spiking transformer with spatial-temporal attention,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:00.405441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f10dd14-b887-4ab3-b681-f5f826ff8a37 · outbound

This paper cites Stca- SNN: self-attention-based temporal-channel joint attention for spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Stca- SNN: self-attention-based temporal-channel joint attention for spiking neural networks,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:40:00.120848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 00efa50b-ef7a-4ec0-bd15-bd613d119439 · outbound

This paper cites Tactilegcn: A graph convolutional network for predicting grasp stability with tactile sensors,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Tactilegcn: A graph convolutional network for predicting grasp stability with tactile sensors,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:59.846187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f39074e0-daa2-4824-a9ba-8af4f606a846 · outbound

This paper cites Slayer: Spike layer error reassignment in time,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Slayer: Spike layer error reassignment in time,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:57.305618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64f01d5c-5d2b-4be1-98a6-f1b5b33472f8 · outbound

This paper cites Event-driven tactile sensing with dense spiking graph neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Event-driven tactile sensing with dense spiking graph neural networks,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:59.614961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6194488d-52de-4fb9-90f7-129bcc83344b · outbound

This paper cites Recurrent bilinear optimization for binary neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Recurrent bilinear optimization for binary neural networks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:59.348821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6977f6e3-df5b-403a-b0a6-830f9bc56eb4 · outbound

This paper cites Inductive representation learning on large graphs,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Inductive representation learning on large graphs,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:57.578425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4647f3ba-cc6a-41ab-b880-a4d88d85c5b6 · outbound

This paper cites Attention spiking neural networks,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Attention spiking neural networks,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:57.622873Z

Source-reported events for the cited work

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Observation cdd6f8c5-98df-4fe7-8e06-39aa6fcdb37d · outbound

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

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping 1.1 computing’s energy problem (and what we can do about it),

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:59.104517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 53ceecb9-f3bf-44de-a609-5005c45e35ca · outbound

This paper cites Toward high-accuracy and low-latency spiking neural networks with two-stage optimization,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Toward high-accuracy and low-latency spiking neural networks with two-stage optimization,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:58.726252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c147296a-c647-4b8e-b0e4-dc1c9a84574f · outbound

This paper cites A million spiking-neuron integrated circuit with a scalable communication network and interface,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping A million spiking-neuron integrated circuit with a scalable communication network and interface,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:57.806367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7715b693-8723-4918-a0ef-17090753668a · outbound

This paper cites The spinnaker project,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping The spinnaker project,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-05T18:39:57.913912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e4e0344-d130-4e61-aea4-217346ed0f18 · outbound

This paper cites Efficient neuromorphic signal processing with loihi 2,.

Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping Efficient neuromorphic signal processing with loihi 2,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:39:58.513380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Reference resolution

Outbound references

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