Paper Citation Record · LEDGER

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 2 inbound Pith citation observations for arXiv:2506.13440.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.13440 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:06:26.490745Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:34:45.973760Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T18:08:46.763350Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy15
unresolved18
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38a598f1-45fc-40ef-b2f9-41e4c8a2d2f8 · outbound

This paper cites Event- based vision: A survey,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Event- based vision: A survey,

Reference 1

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Source-reported events for the cited work

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Observation 3b77de92-ba6c-4aae-991e-d680856a9fb7 · outbound

This paper cites Learning to detect objects with a 1 megapixel event camera,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Learning to detect objects with a 1 megapixel event camera,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.892495Z

Source-reported events for the cited work

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Observation 9fc1852a-361f-487c-a1e9-161a854a2a14 · outbound

This paper cites A Large Scale Event-based Detection Dataset for Automotive.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection A Large Scale Event-based Detection Dataset for Automotive

Reference 3

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Observation cb314341-a7c6-4bb9-ae9b-683432528762 · outbound

This paper cites Asynchronous spatio-temporal memory network for continuous event-based object detection,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Asynchronous spatio-temporal memory network for continuous event-based object detection,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.366571Z

Source-reported events for the cited work

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Observation e5a09422-f114-40cb-b4d3-0247a310832e · outbound

This paper cites Recurrent vision transformers for object detection with event cameras,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Recurrent vision transformers for object detection with event cameras,

Reference 5

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Observation a9998446-0ab8-4ea3-b79d-57a26f5ef489 · outbound

This paper cites Event-based optical flow on neuromorphic processor: Ann vs. snn comparison based on activation sparsification,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Event-based optical flow on neuromorphic processor: Ann vs. snn comparison based on activation sparsification,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.861321Z

Source-reported events for the cited work

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Observation 32723c31-b04a-42ab-bd09-89d055994c78 · outbound

This paper cites Seneca: building a fully digital neuromorphic processor, design trade-offs and challenges,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Seneca: building a fully digital neuromorphic processor, design trade-offs and challenges,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.846976Z

Source-reported events for the cited work

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Observation 848d1102-4072-404e-bb7e-9178fe8666f4 · outbound

This paper cites Object detection with spiking neural networks on automotive event data,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Object detection with spiking neural networks on automotive event data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.833860Z

Source-reported events for the cited work

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Observation 29e2eadc-96bb-4378-a9e9-7c59baadc12a · outbound

This paper cites Ef- ficient recurrent architectures through activity sparsity and sparse back- propagation through time,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Ef- ficient recurrent architectures through activity sparsity and sparse back- propagation through time,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.820865Z

Source-reported events for the cited work

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Observation d9bf725f-ea3e-4909-92f3-ae7be013ea2d · outbound

This paper cites Memory- efficient deep learning on a spinnaker 2 prototype,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Memory- efficient deep learning on a spinnaker 2 prototype,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.807824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ab4b037a-e71f-4032-8111-2c70fc26b1b9 · outbound

This paper cites How many events make an object? improving single-frame object detection on the 1 mpx dataset,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection How many events make an object? improving single-frame object detection on the 1 mpx dataset,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.793683Z

Source-reported events for the cited work

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Observation 2678946d-d955-4a8b-a84d-517dc8090da7 · outbound

This paper cites From chaos comes order: Ordering event representations for object recognition and detection,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection From chaos comes order: Ordering event representations for object recognition and detection,

Reference 12

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Observation 1b9369a5-b7d8-4447-a36c-ad52101ed684 · outbound

This paper cites Accelerating convolutional neural networks via activa- tion map compression,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Accelerating convolutional neural networks via activa- tion map compression,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.770415Z

Source-reported events for the cited work

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Observation ef5dc44f-248d-4f70-a8cb-8e66eabc5fe9 · outbound

This paper cites Inducing and exploiting activation sparsity for fast inference on deep neural networks,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Inducing and exploiting activation sparsity for fast inference on deep neural networks,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.410680Z

Source-reported events for the cited work

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Observation 450059f2-ba64-4eb2-a07c-6d13a734ddbe · outbound

This paper cites Learning phrase representations using rnn encoder-decoder for statistical machine translation,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Learning phrase representations using rnn encoder-decoder for statistical machine translation,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.414735Z

Source-reported events for the cited work

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Observation 673d670b-931b-4201-98b8-a8f669cd53ef · outbound

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

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 16

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Observation 6c1e267e-8703-4e06-a7ae-e2c9e584eb5d · outbound

This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.422634Z

Source-reported events for the cited work

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Observation 4163a8e9-02b1-4cb4-86a6-9249feca5771 · outbound

This paper cites Nxtf: An api and compiler for deep spiking neural networks on intel loihi,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Nxtf: An api and compiler for deep spiking neural networks on intel loihi,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.721534Z

Source-reported events for the cited work

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Observation 0fa3954a-cf6f-4526-835e-6977239a0843 · outbound

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

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Towards artificial general intelligence with hybrid tianjic chip architecture,

Reference 19

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Observation 87ac7a5b-c3d5-45ff-8c76-663ef58532ac · outbound

This paper cites Ssd: Single shot multibox detector,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Ssd: Single shot multibox detector,

Reference 20

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Observation 0c51cd7a-c468-4f6b-ac63-a849d2f769a9 · outbound

This paper cites Hats: Histograms of averaged time surfaces for robust event-based ob- ject classification,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Hats: Histograms of averaged time surfaces for robust event-based ob- ject classification,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.439240Z

Source-reported events for the cited work

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Observation 2364c758-01c6-4993-824c-62f7fdfdf11c · outbound

This paper cites High speed and high dynamic range video with an event camera,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection High speed and high dynamic range video with an event camera,

Reference 22

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Observation 8de7c195-fab4-4643-94fb-2013465fe8a5 · outbound

This paper cites Long Short-Term Memory,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Long Short-Term Memory,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.448329Z

Source-reported events for the cited work

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Observation b89e4587-afd3-4fac-993c-edb2f2427526 · outbound

This paper cites Minimal gated unit for recurrent neural networks,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Minimal gated unit for recurrent neural networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T20:06:26.452326Z

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Observation 1b3f639c-d4b3-4008-b382-a7494b24e5ad · outbound

This paper cites MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks

Reference 25

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Source-reported events for the cited work

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Observation f4d92538-97a7-4653-bd81-6b55e9479b7f · outbound

This paper cites Reducing information loss for spiking neural networks,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Reducing information loss for spiking neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.664991Z

Source-reported events for the cited work

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Observation 62293b17-8f02-4c61-b894-4cb9a8d9f1df · outbound

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

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Integer-valued training and spike-driven inference spiking neural network for high-performance and energy-efficient object detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.652004Z

Source-reported events for the cited work

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Observation 9ce85bbe-ab71-4f04-be41-f864fb8d6600 · outbound

This paper cites Microsoft coco: Common objects in context,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Microsoft coco: Common objects in context,

Reference 28

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Observation 72bcfbd1-9433-4d47-b02b-d02f20cfcf0f · outbound

This paper cites Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.630490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 769d6c97-45b3-4dce-a9d7-bdc66f694759 · outbound

This paper cites Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.606797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 587d4622-9082-4662-8595-f71ed4843158 · outbound

This paper cites Squeeze-and-excitation networks,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Squeeze-and-excitation networks,

Reference 31

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Observation bc677c70-ba38-461e-a166-a61e3321ce12 · outbound

This paper cites Opportunities for neuromorphic computing algorithms and applica- tions,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Opportunities for neuromorphic computing algorithms and applica- tions,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.583489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 750f0cc4-69a1-4385-a6ba-de44e517ca8c · outbound

This paper cites Better and faster: Adap- tive event conversion for event-based object detection,.

Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection Better and faster: Adap- tive event conversion for event-based object detection,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:06:26.568930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation a8b19335-ba1b-4a7f-b650-0a5d7dfaea35 · inbound

Context-aware Sparse Spatiotemporal Learning for Event-based Vision cites this paper.

Context-aware Sparse Spatiotemporal Learning for Event-based Vision Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection

Reference 8

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Observation 1056b604-5095-4585-b8ce-2067b1aa5277 · inbound

FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection cites this paper.

FATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-03T18:08:46.764935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.