Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:31.482523Z
Paper Citation Record · LEDGER
The Resurrection of the ReLU
As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2505.22074.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-22T09:15:32.395442Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T09:16:19.683438Z
Reference resolution
43 of 43 outbound references displayed
- verified exact0
- verified fuzzy29
- unresolved13
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3020e82f-74de-4d0f-ae3b-aae44842183c · outbound
This paper cites Long short-term memory and learning-to-learn in networks of spiking neurons.Advances in neural information processing systems, 31, 2018.
The Resurrection of the ReLU Long short-term memory and learning-to-learn in networks of spiking neurons.Advances in neural information processing systems, 31, 2018
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.058024Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e472a346-7005-477d-b192-200cdfbebbaf · outbound
This paper cites Fast and accurate deep network learning by exponential linear units (elus).
The Resurrection of the ReLU Fast and accurate deep network learning by exponential linear units (elus)
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:37.676599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8917d30c-f528-4940-9b63-ed8548aad998 · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
The Resurrection of the ReLU Imagenet: A large-scale hierarchical image database
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.246212Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be2f8934-4110-4be0-875d-8bb5ecbf84d9 · outbound
This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural Networks, 107:3–11, 2018.
The Resurrection of the ReLU Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural Networks, 107:3–11, 2018
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:37.484207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c72a03a2-4d93-40e8-83d2-a4b4272d59af · outbound
This paper cites Conv2next: Reconsidering conv next network design for image recognition.
The Resurrection of the ReLU Conv2next: Reconsidering conv next network design for image recognition
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:37.250068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation cab59ff9-327e-4ea6-ad2b-3bc796fc25d7 · outbound
This paper cites The State of Sparsity in Deep Neural Networks.
The Resurrection of the ReLU The State of Sparsity in Deep Neural Networks
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.540892Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbe9bbba-158d-49f5-a4f4-02c79e758562 · outbound
This paper cites Deep sparse rectifier neural networks.
The Resurrection of the ReLU Deep sparse rectifier neural networks
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:37.045339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 96863180-587d-4f81-a6a7-89f2097bd3b0 · outbound
This paper cites Zhang, Shaoqing Ren, and Jian Sun.
The Resurrection of the ReLU Zhang, Shaoqing Ren, and Jian Sun
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.764443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 82483c58-60a4-409b-8918-f35d9e1de4bc · outbound
This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.
The Resurrection of the ReLU Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.874323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 80240f8e-3f44-43ee-b8a1-e04367a18c4d · outbound
This paper cites Deep residual learning for image recognition.
The Resurrection of the ReLU Deep residual learning for image recognition
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:28.977035Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4033a237-4c74-49a2-9272-064eeeb211a7 · outbound
This paper cites Gaussian Error Linear Units (GELUs).
The Resurrection of the ReLU Gaussian Error Linear Units (GELUs)
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.063077Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ad3feeb-b778-4c4e-a268-7d10c32b5f5d · outbound
This paper cites Balanced resonate-and-fire neurons.
The Resurrection of the ReLU Balanced resonate-and-fire neurons
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.729204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 8541847c-e5b5-4d2e-81ba-e415909f49ec · outbound
This paper cites Foerster, and Yarin Gal.
The Resurrection of the ReLU Foerster, and Yarin Gal
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.543625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1e65c008-26a9-446a-9666-cca6f5f10344 · outbound
This paper cites Relu’s revival: On the entropic overload in normalization-free large language models.2nd Workshop on Attributing Model Behavior at Scale (NeurIPS), 2024.
The Resurrection of the ReLU Relu’s revival: On the entropic overload in normalization-free large language models.2nd Workshop on Attributing Model Behavior at Scale (NeurIPS), 2024
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.389386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 56d9b94a-8615-45bc-816a-e88fa61253c5 · outbound
This paper cites Warp-lca: Efficient convolutional sparse coding with locally competitive algorithm.Neurocomputing, page 130291, 2025.
The Resurrection of the ReLU Warp-lca: Efficient convolutional sparse coding with locally competitive algorithm.Neurocomputing, page 130291, 2025
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.247100Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5e52bd71-7cec-4d5d-bf4c-5c026e870f9c · outbound
This paper cites Adam: A Method for Stochastic Optimization.
The Resurrection of the ReLU Adam: A Method for Stochastic Optimization
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.506912Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c54d3c66-a0c2-4086-9023-feb4b9a905e6 · outbound
This paper cites Self-normalizing neural networks.
The Resurrection of the ReLU Self-normalizing neural networks
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:36.060851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 55cc4989-a224-4121-9547-7f3d767b2d61 · outbound
This paper cites Learning multiple layers of features from tiny images.
The Resurrection of the ReLU Learning multiple layers of features from tiny images
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:35.913884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation f4402a56-6b81-4b2a-9978-21500f3810b3 · outbound
This paper cites Imagenet classification with deep convolutional neural networks.
The Resurrection of the ReLU Imagenet classification with deep convolutional neural networks
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:35.731145Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b4829815-5a17-4556-8f86-d2636b7e0459 · outbound
This paper cites an unresolved cited work.
The Resurrection of the ReLU Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:21:35.553034Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 582990b8-d4d5-4d97-bebf-11bd16c7ff37 · outbound
This paper cites Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31, 2018.
The Resurrection of the ReLU Visualizing the loss landscape of neural nets.Advances in neural information processing systems, 31, 2018
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:29.851420Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73fc2b71-8740-462d-91e1-2489d92fc6d1 · outbound
This paper cites Leaky relus that differ in forward and backward pass facilitate activation maximization in deep neural networks.
The Resurrection of the ReLU Leaky relus that differ in forward and backward pass facilitate activation maximization in deep neural networks
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:35.342877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5c679e30-56f4-45e1-9818-7561a74b94da · outbound
This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021.
The Resurrection of the ReLU Swin transformer: Hierarchical vision transformer using shifted windows.2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9992–10002, 2021
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:35.173304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation de240235-f27c-4014-8d8c-40a1344b14fb · outbound
This paper cites A convnet for the 2020s.
The Resurrection of the ReLU A convnet for the 2020s
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:34.923993Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dcf5ceb3-c7a9-4c6e-9a72-7f5a360b4f15 · outbound
This paper cites Dying relu and initialization: Theory and numerical examples.Communications in Computational Physics, 28(5):1671–1706, January 2020.
The Resurrection of the ReLU Dying relu and initialization: Theory and numerical examples.Communications in Computational Physics, 28(5):1671–1706, January 2020
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:34.704268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 5ee9d6e0-18ab-490f-808b-d47aca776997 · outbound
This paper cites Rectifier nonlinearities improve neural network acoustic models.
The Resurrection of the ReLU Rectifier nonlinearities improve neural network acoustic models
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:34.484249Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9a8ecb7f-a609-4224-9721-6e13bdc6e7f3 · outbound
This paper cites On implicit filter level sparsity in convolutional neural networks.
The Resurrection of the ReLU On implicit filter level sparsity in convolutional neural networks
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:34.291413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 200aa679-d618-4fc9-9818-943bd201deb3 · outbound
This paper cites On the number of linear regions of deep neural networks.
The Resurrection of the ReLU On the number of linear regions of deep neural networks
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:34.033790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2b2fda66-f425-4f96-a431-f728f5c02d08 · outbound
This paper cites Topology of deep neural networks.J.
The Resurrection of the ReLU Topology of deep neural networks.J
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:33.801269Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 3ddc8800-7aa7-4c53-9677-a3227dba0446 · outbound
This paper cites Neftci, Hesham Mostafa, and Friedemann Zenke.
The Resurrection of the ReLU Neftci, Hesham Mostafa, and Friedemann Zenke
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:33.586081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9d7c44d3-799b-4fba-8c3a-f36134f089af · outbound
This paper cites Searching for activation functions.
The Resurrection of the ReLU Searching for activation functions
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:33.388443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 67b43e3d-155c-41c8-be39-bef9b040484a · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation, 2015.
The Resurrection of the ReLU U-net: Convolutional networks for biomedical image segmentation, 2015
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:30.686699Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9317e119-6e2e-4467-9640-349125b054d7 · outbound
This paper cites Sparse coding via thresholding and local competition in neural circuits.Neural computation, 20(10):2526–2563, 2008.
The Resurrection of the ReLU Sparse coding via thresholding and local competition in neural circuits.Neural computation, 20(10):2526–2563, 2008
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:33.198625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4c2e7f16-b4e5-4a08-a6f5-fbdcdeeb667f · outbound
This paper cites Flexible and efficient surrogate gradient modeling with forward gradient injection.
The Resurrection of the ReLU Flexible and efficient surrogate gradient modeling with forward gradient injection
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:32.980844Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 569575f7-ca1d-4668-b320-da68e7a37fc3 · outbound
This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.
The Resurrection of the ReLU Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:30.948163Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58580992-01bd-424a-b232-b3310d415a69 · outbound
This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.
The Resurrection of the ReLU Attention is all you need.Advances in neural information processing systems, 30, 2017
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:31.003806Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f640565-15ec-4479-bc90-d8703954e85e · outbound
This paper cites Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016.
The Resurrection of the ReLU Learning structured sparsity in deep neural networks.Advances in neural information processing systems, 29, 2016
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:31.072019Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ae25dd8-60a9-4e99-8c3e-2e4f5f174813 · outbound
This paper cites Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.Nature Machine Intelligence, 3(10):905–913, 2021.
The Resurrection of the ReLU Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks.Nature Machine Intelligence, 3(10):905–913, 2021
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:32.579655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 70f0638b-c6ef-4ecf-b6a2-caa4a7b7db42 · outbound
This paper cites Zeiler, M.
The Resurrection of the ReLU Zeiler, M
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:32.437516Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 0e4d42ac-0dda-498b-b50c-02cdfb98bbf7 · outbound
This paper cites l_{1/2} regularization: Convergence of iterative half thresholding algorithm.IEEE Transactions on Signal Processing, 62(9):2317–2329, 2014.
The Resurrection of the ReLU l_{1/2} regularization: Convergence of iterative half thresholding algorithm.IEEE Transactions on Signal Processing, 62(9):2317–2329, 2014
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:32.291140Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation d63862d7-7350-41b5-bbb9-170b63a02802 · outbound
This paper cites The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.Neural computation, 33(4):899–925, 2021.
The Resurrection of the ReLU The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.Neural computation, 33(4):899–925, 2021
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:32.133427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2004d889-503e-4398-af6b-9187eb4ee226 · outbound
This paper cites Tropical geometry of deep neural networks.
The Resurrection of the ReLU Tropical geometry of deep neural networks
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:31.952164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation be4ec0bf-7706-4b32-a826-36514475bca3 · outbound
This paper cites For each function, we drew 3000 samples from U[− √ 3, √ 3]din, used as input for the network.
The Resurrection of the ReLU For each function, we drew 3000 samples from U[− √ 3, √ 3]din, used as input for the network
Reference 43
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T13:21:31.784038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
Observation 263a3459-1039-41b9-8018-dd4af82abb8f · inbound
Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes cites this paper.
Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes The Resurrection of the ReLU
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T13:48:19.613726Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 1483b5ad-a82a-4383-9613-1ea32e911537 · inbound
Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes cites this paper.
Bug or Feature$^2$: Weight Drift, Activation Sparsity and Spikes The Resurrection of the ReLU
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T09:16:19.686754Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.