Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields | Paper Citation Record

Observation 8100a02f-8fe8-4d1e-aa58-ee99ee3d5fe3 · outbound

This paper cites Mip-nerf: A multiscale representation for anti-aliasingneuralradiancefields,in:ProceedingsoftheIEEE/CVF international conference on computer vision, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Mip-nerf: A multiscale representation for anti-aliasingneuralradiancefields,in:ProceedingsoftheIEEE/CVF international conference on computer vision, pp

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.423293Z

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Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:57.407422Z

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Observation 4ea7e641-9581-408d-807a-19135af5d615 · outbound

This paper cites Zip-nerf: Anti-aliased grid-based neural radiance fields, in: ProceedingsoftheIEEE/CVFInternationalConferenceonComputer Vision, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Zip-nerf: Anti-aliased grid-based neural radiance fields, in: ProceedingsoftheIEEE/CVFInternationalConferenceonComputer Vision, pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.376320Z

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Observation b0cd78c1-a6ee-41da-8de4-b61d2e45137e · outbound

This paper cites Sah- nerf: Enhancing nerf on novel view synthesis with an snn-ann hybrid framework.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Sah- nerf: Enhancing nerf on novel view synthesis with an snn-ann hybrid framework

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.360077Z

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Observation 865936a8-4f95-4e49-91d9-7e6faab7e90a · outbound

This paper cites Tensorf: Ten- sorial radiance fields, in: European conference on computer vision, Springer.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Tensorf: Ten- sorial radiance fields, in: European conference on computer vision, Springer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.345509Z

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Observation a52ea2e2-71d8-4c4f-981c-9bfd66b6f501 · outbound

This paper cites How far can we compress instant-ngp-based nerf?, in: Proceedings of the IEEE/CVF ConferenceonComputerVisionandPatternRecognition,pp.20321– 20330.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields How far can we compress instant-ngp-based nerf?, in: Proceedings of the IEEE/CVF ConferenceonComputerVisionandPatternRecognition,pp.20321– 20330

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.329848Z

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Observation bc7be9e7-b936-41d6-9d08-a0ca98dfc94d · outbound

This paper cites Sres-nerf: improved neuralradiancefieldsforrealismandaccuracyofspecularreflections, in: International Conference on Multimedia Modeling, Springer.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Sres-nerf: improved neuralradiancefieldsforrealismandaccuracyofspecularreflections, in: International Conference on Multimedia Modeling, Springer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.314405Z

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Observation 49dc5270-419b-4199-ab91-db41b42b5fa1 · outbound

This paper cites Candeepneuralnetworksbeconverted to ultra low-latency spiking neural networks?, in: 2022 Design, Au- tomation & Test in Europe Conference & Exhibition (DATE), IEEE.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Candeepneuralnetworksbeconverted to ultra low-latency spiking neural networks?, in: 2022 Design, Au- tomation & Test in Europe Conference & Exhibition (DATE), IEEE

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.298830Z

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Observation 7c159164-48be-4803-8e5d-b0cace478550 · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:57.282939Z

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Observation d200be20-bf56-404d-8147-47dc838cb0df · outbound

This paper cites JaxNeRF: an ef- ficient JAX implementation of NeRF.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields JaxNeRF: an ef- ficient JAX implementation of NeRF

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.264944Z

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Observation d59c3767-916c-4523-84df-06997c3b6b26 · outbound

This paper cites Auditory perception architecturewithspikingneuralnetworkandimplementationonfpga.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Auditory perception architecturewithspikingneuralnetworkandimplementationonfpga

Reference 11

Resolution: verified exact; doi, observed 2026-08-06T11:15:55.766273Z

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Observation 79cda663-bdfe-4b56-8900-f1be1343eab1 · outbound

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

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Reference 12

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Observation 5c720266-72a0-43ce-a4a4-4ad674fe7c0f · outbound

This paper cites Spikingjelly: An open-source machinelearninginfrastructureplatformforspike-basedintelligence.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spikingjelly: An open-source machinelearninginfrastructureplatformforspike-basedintelligence

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.250198Z

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Observation 7b22d77f-3eb4-47de-97ca-52648b745495 · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:57.235303Z

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Observation 77297a9a-cc16-41f3-9787-6d56db130a32 · outbound

This paper cites Plenoxels: Radiance fields without neural networks,in:ProceedingsoftheIEEE/CVFConferenceonComputer Vision and Pattern Recognition, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Plenoxels: Radiance fields without neural networks,in:ProceedingsoftheIEEE/CVFConferenceonComputer Vision and Pattern Recognition, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.203747Z

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Observation e795093f-cb24-424c-96a8-265e13b59494 · outbound

This paper cites Shacira: Scalable hash- grid compression for implicit neural representations, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Shacira: Scalable hash- grid compression for implicit neural representations, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.186274Z

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Observation d93c7ed5-65f7-4893-a468-b205913d8f5b · outbound

This paper cites Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:15:56.295313Z

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Observation cf9ec102-a5be-489a-ae21-8ec7a22ade0b · outbound

This paper cites Spike-nerf: Neural radiance field based on spike camera, in: 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spike-nerf: Neural radiance field based on spike camera, in: 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.170461Z

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Observation d3d7f36a-58c4-4771-b9d2-0896604fcf2d · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:57.154086Z

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Observation 57d74de1-26d7-4547-b5c8-7133c0efed5c · outbound

This paper cites G-NeLF: Memory- and Data-Efficient Hybrid Neural Light Field for Novel View Synthesis.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields G-NeLF: Memory- and Data-Efficient Hybrid Neural Light Field for Novel View Synthesis

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:15:56.267499Z

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Observation 725f6bf0-a038-4fc7-8109-b00d6c26508e · outbound

This paper cites Spikingneuralnetworksonfpga:A survey of methodologies and recent advancements.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spikingneuralnetworksonfpga:A survey of methodologies and recent advancements

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:15:56.244375Z

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Observation c76e33f6-6fec-4982-91df-de072ba37f92 · outbound

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Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:57.138940Z

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Observation 13270e25-c383-40eb-a311-f313cf6c1707 · outbound

This paper cites Unleashing the potential of spiking neural networks with dynamic confidence, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unleashing the potential of spiking neural networks with dynamic confidence, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.108619Z

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Observation 997c65de-2271-473a-ac76-34e79774352f · outbound

This paper cites Seenn:towardstemporal spiking early exit neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Seenn:towardstemporal spiking early exit neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.092995Z

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Observation 6e366239-825c-4acd-8ca5-e91cc2cf0f2b · outbound

This paper cites Input-aware dynamic timestep spiking neural networks for efficient in-memory computing, in: 2023 60th ACM/IEEE Design Automation Conference (DAC), IEEE.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Input-aware dynamic timestep spiking neural networks for efficient in-memory computing, in: 2023 60th ACM/IEEE Design Automation Conference (DAC), IEEE

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.073835Z

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Observation 6f93737d-253d-4dc6-ace0-31881413e25e · outbound

This paper cites Spiking-nerf: Spiking neural network for energy-efficient neural rendering.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spiking-nerf: Spiking neural network for energy-efficient neural rendering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.056274Z

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Observation a287481f-984e-4681-9c15-75f3e60d9651 · outbound

This paper cites Im-lif: Improved neuronal dynamics with attention mechanism for direct training deep spiking neural network.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Im-lif: Improved neuronal dynamics with attention mechanism for direct training deep spiking neural network

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.038025Z

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 00d47e70-3565-4bba-92b6-50cafcf24824 · outbound

This paper cites Spiking nerf: Repre- senting the real-world geometry by a discontinuous representation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spiking nerf: Repre- senting the real-world geometry by a discontinuous representation, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.019261Z

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 d7982e61-7784-4099-95b3-d32c66c08fc3 · outbound

This paper cites Teas: Exploiting spiking activity for temporal-wise adaptive spiking neural networks, in: 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Teas: Exploiting spiking activity for temporal-wise adaptive spiking neural networks, in: 2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.881010Z

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Observation fbf865ef-01ce-441a-bdfa-3a0939249318 · outbound

This paper cites Content-aware radi- ance fields: Aligning model complexity with scene intricacy through learnedbitwidthquantization,in:EuropeanConferenceonComputer Vision, Springer.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Content-aware radi- ance fields: Aligning model complexity with scene intricacy through learnedbitwidthquantization,in:EuropeanConferenceonComputer Vision, Springer

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.865252Z

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Observation f4a3182a-deab-4b1e-9880-7075c85c74c6 · outbound

This paper cites Spalen: Sp arsity a ware l oad balancing inference e ngine for neural network.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spalen: Sp arsity a ware l oad balancing inference e ngine for neural network

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.846239Z

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Observation 21c337cb-def2-4a70-b16c-7cf2e22eda19 · outbound

This paper cites Local light field fusion: Practicalviewsynthesiswithprescriptivesamplingguidelines.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Local light field fusion: Practicalviewsynthesiswithprescriptivesamplingguidelines

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T11:15:55.574805Z

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Observation 4abc1804-7d3c-4582-9619-20ea00f8cb4c · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view synthesis.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Nerf: Representing scenes as neural radiance fields for view synthesis

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.829422Z

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 ce20e1d6-711c-48f0-bf27-9e306f320b81 · outbound

This paper cites Instant neural graphics primitives with a multiresolution hash encoding.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Instant neural graphics primitives with a multiresolution hash encoding

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.813464Z

Source-reported events for the cited work

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Observation 0fce443a-0a9c-470f-bafb-21c517e9bf65 · outbound

This paper cites Surrogate gradient learn- inginspikingneuralnetworks:Bringingthepowerofgradient-based optimization to spiking neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Surrogate gradient learn- inginspikingneuralnetworks:Bringingthepowerofgradient-based optimization to spiking neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.797611Z

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

Observation b7ca17c0-11ad-46a1-89e6-90ce8438ad16 · outbound

This paper cites Self-architecturalknowledgedistillationfor spikingneuralnetworks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Self-architecturalknowledgedistillationfor spikingneuralnetworks

Reference 36

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T11:15:56.080926Z

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Observation 22d98be2-ce6f-4b7a-949b-5cfc3198313d · outbound

This paper cites Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.781327Z

Source-reported events for the cited work

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Observation c571021d-ab16-4d68-9904-bb43e1b915d1 · outbound

This paper cites Binary opacity grids:Capturingfinegeometricdetailformesh-basedviewsynthesis.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Binary opacity grids:Capturingfinegeometricdetailformesh-basedviewsynthesis

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.765480Z

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 839bb17f-3af9-4118-8e7f-4ec5a0159c20 · outbound

This paper cites Spikingresformer:bridgingresnetand vision transformer in spiking neural networks, in: Proceedings of the IEEE/CVFConferenceonComputerVisionandPatternRecognition, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spikingresformer:bridgingresnetand vision transformer in spiking neural networks, in: Proceedings of the IEEE/CVFConferenceonComputerVisionandPatternRecognition, pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.748622Z

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 95792d6a-a6f3-44d7-b504-5072153fcf45 · outbound

This paper cites Binary radiance fields.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Binary radiance fields

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.730428Z

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 3f66947e-e40a-4d6e-869b-47627e238bbe · outbound

This paper cites Scene representa- tion networks: Continuous 3d-structure-aware neural scene represen- tations, in: Advances in Neural Information Processing Systems.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Scene representa- tion networks: Continuous 3d-structure-aware neural scene represen- tations, in: Advances in Neural Information Processing Systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.711166Z

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 fdfd996e-68af-4be7-9bac-112764356b04 · outbound

This paper cites Variablebitrateneuralfields,in:ACM SIGGRAPH 2022 Conference Proceedings, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Variablebitrateneuralfields,in:ACM SIGGRAPH 2022 Conference Proceedings, pp

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.693123Z

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 e50f0918-acd1-4cf1-8815-4f2102e756f1 · outbound

This paper cites Nerfstudio: A modular framework for neural radiance field development, in: ACM SIGGRAPH 2023 conference proceedings, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Nerfstudio: A modular framework for neural radiance field development, in: ACM SIGGRAPH 2023 conference proceedings, pp

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.675279Z

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 88419aa9-6c9d-42fe-8da2-fa66f47215a2 · outbound

This paper cites Torch-ngp: a pytorch implementation of instant-ngp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Torch-ngp: a pytorch implementation of instant-ngp

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.659388Z

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 75816dad-d4bf-4a7c-9c13-3384f9810f8e · outbound

This paper cites Deep learning in spiking neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Deep learning in spiking neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.642451Z

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 668b2724-c4e5-4b3a-add6-3e7a76e57ab8 · outbound

This paper cites Spik-nerf: Spiking neural networks for neural radiance fields.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spik-nerf: Spiking neural networks for neural radiance fields

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.621923Z

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 d547abd9-14f3-427b-b982-7e58586ed94f · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:56.602910Z

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Observation 806d91fe-5c60-4175-8d31-fc3e41337e5c · outbound

This paper cites Spatio-temporalback- propagation for training high-performance spiking neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Spatio-temporalback- propagation for training high-performance spiking neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.583671Z

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

Observation 562bd9ae-2745-491b-bb3e-c96c1f8fe5ab · outbound

This paper cites Hollownerf:Pruning hashgrid-based nerfs with trainable collision mitigation, in: Proceed- ingsoftheIEEE/CVFInternationalConferenceonComputerVision, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Hollownerf:Pruning hashgrid-based nerfs with trainable collision mitigation, in: Proceed- ingsoftheIEEE/CVFInternationalConferenceonComputerVision, pp

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.563785Z

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

Observation 83fdb74a-cc75-4156-bce6-24157b707c04 · outbound

This paper cites Back- propagation with sparsity regularization for spiking neural network learning.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Back- propagation with sparsity regularization for spiking neural network learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.548156Z

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

Observation c16e91b2-6631-450b-a6f9-70c36d8c73bd · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:56.529852Z

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

Observation aaa22e9c-be85-4ce2-b2a2-8f9c433f9355 · outbound

This paper cites SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T11:15:55.670258Z

Source-reported events for the cited work

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Observation d5633548-ef92-4015-be1f-49e36ac7aa30 · outbound

This paper cites Towards efficient and accurate spiking neural networks via adaptive bit allocation.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Towards efficient and accurate spiking neural networks via adaptive bit allocation

Reference 53

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Observation 79e400e4-e4f7-4ae7-ad32-a622a5f826f0 · outbound

This paper cites Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.484529Z

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 ffdd5b44-fd41-4213-b9bb-82ddc0f40905 · outbound

This paper cites IEEE Transactions on Visualization and Computer Graphics 29, 5124–5136.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields IEEE Transactions on Visualization and Computer Graphics 29, 5124–5136

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.506454Z

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 ad4cfa04-5a96-4b69-8f94-cdf6dc454ad0 · outbound

This paper cites Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:15:55.827564Z

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 469b16a9-41b5-4ab6-993f-95b2915f61f2 · outbound

This paper cites NeRF++: Analyzing and Improving Neural Radiance Fields.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields NeRF++: Analyzing and Improving Neural Radiance Fields

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T11:15:55.693248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89263d71-10c3-4696-810f-7dcff67a9968 · outbound

This paper cites Tc-lif: A two-compartment spiking neuron model for long-term sequential modelling, in: Proceedings of the AAAI conference on artificial intelligence, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Tc-lif: A two-compartment spiking neuron model for long-term sequential modelling, in: Proceedings of the AAAI conference on artificial intelligence, pp

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.444109Z

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 80fa5d8a-62f5-4aee-abec-9a03d2c8f49a · outbound

This paper cites Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.463196Z

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 a8012a66-626a-47f6-8848-fe0f5d54cc9c · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:56.398386Z

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 a816ca9d-c35b-466c-954e-441ad905f127 · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:56.375089Z

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 1b5b3951-5065-4e74-9ce8-1fce4df86521 · outbound

This paper cites Gradient aware adaptive quantization: Locally uniform quantization with learnable clipping thresholds for globally non-uniform weights.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Gradient aware adaptive quantization: Locally uniform quantization with learnable clipping thresholds for globally non-uniform weights

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.358846Z

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 106199f6-470c-43c3-8464-200805365ecb · outbound

This paper cites an unresolved cited work.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:15:56.420727Z

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 35b27615-e453-4523-b397-c89e3754d56b · outbound

This paper cites Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T11:15:55.725638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12064e3c-722f-4d3c-ab25-bffe6d4cca1e · outbound

This paper cites Is vanilla mlp in neural radiance field enough for few-shot view synthesis?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields Is vanilla mlp in neural radiance field enough for few-shot view synthesis?, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:56.340677Z

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 62871d5d-2786-49c3-871f-ab517ebbbccf · outbound

This paper cites 2661– 2671.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields 2661– 2671

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.219220Z

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 f43a015d-d1cd-4d20-85f8-bad3bf013d50 · outbound

This paper cites 5470–5479.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields 5470–5479

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.392001Z

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 d5ae5423-5313-4f59-99e7-b6f13d61e084 · outbound

This paper cites 8308–8316.

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields 8308–8316

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:15:57.123751Z

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.

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