Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T20:07:43.864760Z
Paper Citation Record · LEDGER
Flopping for FLOPs: Leveraging equivariance for computational efficiency
As of 9 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 2 inbound Pith citation observations for arXiv:2502.05169.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T09:59:55.032554Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T10:04:36.113872Z
Reference resolution
60 of 60 outbound references displayed
- verified exact3
- verified fuzzy32
- unresolved25
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- malformed identifier0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f194effd-e436-449b-8493-e14bfb55db20 · outbound
This paper cites Layer Normalization.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Layer Normalization
Reference 1
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e5ec4565-5c89-49e3-b256-328c1cf27d66 · outbound
This paper cites Neural machine translation by jointly learning to align and translate.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Neural machine translation by jointly learning to align and translate
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation dc370d59-d0bb-4351-a0ec-a71c66c0c57f · outbound
This paper cites J., Lafarge, M.
Flopping for FLOPs: Leveraging equivariance for computational efficiency J., Lafarge, M
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 9ad134b9-6bc6-48b7-9f76-4e9631d66c31 · outbound
This paper cites J., Vadgama, S., Hesselink, R., der Linden, P.
Flopping for FLOPs: Leveraging equivariance for computational efficiency J., Vadgama, S., Hesselink, R., der Linden, P
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation a0c0652b-77ca-46cb-9d86-6952f63a06eb · outbound
This paper cites an unresolved cited work.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Unresolved cited work
Reference 5
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Source-reported events for the cited work
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Observation 1a1acf0a-150e-4dea-add2-d568d1ab3ab0 · outbound
This paper cites and Kahl, F.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Kahl, F
Reference 6
- Resolution
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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 43bd55fc-a6e7-4a12-a0ee-edb08103fe8f · outbound
This paper cites Steerers: A framework for rotation equivariant keypoint descriptors.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Steerers: A framework for rotation equivariant keypoint descriptors
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation af5dc562-651a-4d0c-ab53-49119f177add · outbound
This paper cites an unresolved cited work.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Unresolved cited work
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 4ef02e37-1462-4bde-abed-8636bc04453c · outbound
This paper cites Does equivariance matter at scale?.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Does equivariance matter at scale?
Reference 9
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47a95176-8d97-4756-a559-3e34a858874c · outbound
This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Reference 10
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8727f444-869a-4c54-adc5-33f38e662e2b · outbound
This paper cites an unresolved cited work.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Unresolved cited work
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 917ebdbe-69aa-4959-af9b-70f077a06975 · outbound
This paper cites and Welling, M.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Welling, M
Reference 12
- Resolution
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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 3d743a6b-8002-4517-a079-e711eb804275 · outbound
This paper cites and Welling, M.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Welling, M
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation de1cae8a-2c88-4453-a63a-eef5036f707b · outbound
This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.
Flopping for FLOPs: Leveraging equivariance for computational efficiency FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Reference 14
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2acfc953-4add-4147-ac50-3222d496739d · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Imagenet: A large-scale hierarchical image database
Reference 15
- Resolution
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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 cfa3dcb7-0444-49c6-b1ba-22589f59d75a · outbound
This paper cites Exploiting cyclic symmetry in convolutional neural networks.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Exploiting cyclic symmetry in convolutional neural networks
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation e1407d98-14cd-40e5-85b5-8095738a264e · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
Flopping for FLOPs: Leveraging equivariance for computational efficiency An image is worth 16x16 words: Transformers for image recognition at scale
Reference 17
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b8aaa82-b740-4653-af6a-fcf4b76bc311 · outbound
This paper cites Cognitron: A self-organizing multilayered neural network.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Cognitron: A self-organizing multilayered neural network
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation c9d461e5-a4b4-466e-8fa4-a76e70df02ef · outbound
This paper cites Learning and Leveraging World Models in Visual Representation Learning.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Learning and Leveraging World Models in Visual Representation Learning
Reference 19
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a2f4fd63-7ab6-4f62-989d-fd3d4c8b59ad · outbound
This paper cites E., Aronsson, J., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., and Persson, D.
Flopping for FLOPs: Leveraging equivariance for computational efficiency E., Aronsson, J., Carlsson, O., Linander, H., Ohlsson, F., Petersson, C., and Persson, D
Reference 20
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 5c8f1333-7035-4698-b1ba-28ce30c8b554 · outbound
This paper cites A., Goldblum, M., and Wilson, A.
Flopping for FLOPs: Leveraging equivariance for computational efficiency A., Goldblum, M., and Wilson, A
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation cb9a91e5-6404-4919-b52b-df087c55e05d · outbound
This paper cites Deep residual learning for image recognition.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Deep residual learning for image recognition
Reference 22
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d898a4f-6677-42e7-bcb8-1023b339ff41 · outbound
This paper cites Identity Mappings in Deep Residual Networks.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Identity Mappings in Deep Residual Networks
Reference 23
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 233f5f48-8190-470f-84e2-ba9f968ebbbf · outbound
This paper cites Gaussian Error Linear Units (GELUs).
Flopping for FLOPs: Leveraging equivariance for computational efficiency Gaussian Error Linear Units (GELUs)
Reference 24
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1558379c-efaa-43d9-9dca-bce7d72653be · outbound
This paper cites Scaling Laws for Neural Language Models.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Scaling Laws for Neural Language Models
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09e3e380-ef6d-4ed3-980b-8d19b6b33e4b · outbound
This paper cites Y., Platt, R., and Walters, R.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Y., Platt, R., and Walters, R
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation ac625500-49fe-4676-a55d-23e198f2a511 · outbound
This paper cites M., Romero, D.
Flopping for FLOPs: Leveraging equivariance for computational efficiency M., Romero, D
Reference 27
- Resolution
- verified fuzzy
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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 d9f86bda-2627-4a8c-b56d-4114b70bd54a · outbound
This paper cites Clebsch gordan nets: a fully fourier space spherical convolutional neural network.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Clebsch gordan nets: a fully fourier space spherical convolutional neural network
Reference 28
- Resolution
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Source-reported events for the cited work
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Observation 48c523e8-3270-49b7-9976-d2b6070abfd8 · outbound
This paper cites and Kondor, R.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Kondor, R
Reference 29
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 820441a3-5b96-4699-b567-44821a638451 · outbound
This paper cites S., Henderson, D., Howard, R.
Flopping for FLOPs: Leveraging equivariance for computational efficiency S., Henderson, D., Howard, R
Reference 30
- Resolution
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Source-reported events for the cited work
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Observation b72e36eb-d519-4cf2-b89d-a10c8f905382 · outbound
This paper cites Learning rotation-equivariant features for visual correspondence.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Learning rotation-equivariant features for visual correspondence
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 84414cef-1f72-495c-b52f-12b786d4d0a2 · outbound
This paper cites and Vedaldi, A.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Vedaldi, A
Reference 32
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6b27d120-aee9-413b-bd35-038368131f0b · outbound
This paper cites and van Gemert, J.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and van Gemert, J
Reference 33
- Resolution
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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 50ebe1c3-6065-4471-8b46-bd83cccf3cc5 · outbound
This paper cites A convnet for the 2020s.
Flopping for FLOPs: Leveraging equivariance for computational efficiency A convnet for the 2020s
Reference 34
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cc8358f-8aae-46a6-84a3-c4ceb44dac1a · outbound
This paper cites an unresolved cited work.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Unresolved cited work
Reference 35
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Source-reported events for the cited work
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Observation 96d5fb9b-2ca4-45d0-88b4-7eb2ec3ba13c · outbound
This paper cites and Eklundh, J.-O.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Eklundh, J.-O
Reference 36
- Resolution
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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 2183db2a-49b2-46fc-ab13-4f84601016f3 · outbound
This paper cites L., Hillar, C.
Flopping for FLOPs: Leveraging equivariance for computational efficiency L., Hillar, C
Reference 37
- Resolution
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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 0de57c2a-44bd-4e4b-a2e4-6b7f515bb1e0 · outbound
This paper cites Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet
Reference 38
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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Flopping for FLOPs: Leveraging equivariance for computational efficiency Naturally occurring equivariance in neural networks
Reference 39
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 061f14cf-62cb-47fb-8a0e-b4dfd4d9c034 · outbound
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Flopping for FLOPs: Leveraging equivariance for computational efficiency Pytorch: An imperative style, high-performance deep learning library
Reference 40
- Resolution
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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 07941478-1476-45d6-a580-1f469870dbe1 · outbound
This paper cites Improving equivariant model training via constraint relaxation.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Improving equivariant model training via constraint relaxation
Reference 41
- Resolution
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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 097d584b-9072-4a85-9b28-7733b590046b · outbound
This paper cites Do ImageNet Classifiers Generalize to ImageNet?.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Do ImageNet Classifiers Generalize to ImageNet?
Reference 42
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 017526c1-8d61-496e-95a1-c5d20510ac6b · outbound
This paper cites Attentive group equivariant convolutional networks.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Attentive group equivariant convolutional networks
Reference 43
- Resolution
- verified fuzzy
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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 e6b0d194-4e60-41b1-9ba0-1beae780363e · outbound
This paper cites and Kroon, R.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Kroon, R
Reference 44
- Resolution
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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 d9433335-45c1-4f11-8a30-8bc8dfbe7020 · outbound
This paper cites Imagenet large scale visual recognition challenge.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Imagenet large scale visual recognition challenge
Reference 45
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfbb6f60-a78b-4fef-bc78-f2ec8bb1a1b4 · outbound
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Flopping for FLOPs: Leveraging equivariance for computational efficiency Linear representations of finite groups
Reference 46
- Resolution
- verified fuzzy
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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 aec485f3-1920-4833-8446-58815244f722 · outbound
This paper cites K., Greff, K., and Schmidhuber, J.
Flopping for FLOPs: Leveraging equivariance for computational efficiency K., Greff, K., and Schmidhuber, J
Reference 47
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41690030-b874-4b48-894d-f67bd9db1441 · outbound
This paper cites The bitter lesson.
Flopping for FLOPs: Leveraging equivariance for computational efficiency The bitter lesson
Reference 48
- Resolution
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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 d53737a0-e363-4735-a37a-acd47b7b76b9 · outbound
This paper cites O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.
Flopping for FLOPs: Leveraging equivariance for computational efficiency O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al
Reference 49
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9ce931b-b071-4f0f-be9c-0113933c6ad1 · outbound
This paper cites Going deeper with Image Transformers.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Going deeper with Image Transformers
Reference 50
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 634c6e50-b155-4d6f-8519-19d5eede9e3a · outbound
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Flopping for FLOPs: Leveraging equivariance for computational efficiency Deit iii: Revenge of the vit
Reference 51
- Resolution
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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 674d5225-960a-42df-b10f-5a95d74d7a99 · outbound
This paper cites Resmlp: Feedforward networks for image classification with data-efficient training.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Resmlp: Feedforward networks for image classification with data-efficient training
Reference 52
- Resolution
- verified exact
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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 7e6ebfa2-c789-4ca0-8dbe-28cb37e1b7db · outbound
This paper cites N., Kaiser, ., and Polosukhin, I.
Flopping for FLOPs: Leveraging equivariance for computational efficiency N., Kaiser, ., and Polosukhin, I
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.722989Z
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 b5b72cfc-ed9b-4f88-8c86-920cc8e7f31a · outbound
This paper cites and Cesa, G.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Cesa, G
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.712603Z
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 54ceafa2-0ef1-4e6b-bc85-37092cb3fad5 · outbound
This paper cites Equivariant and Coordinate Independent Convolutional Networks.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Equivariant and Coordinate Independent Convolutional Networks
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.704494Z
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 babcdf3d-123b-4de7-80c2-8b742df83b12 · outbound
This paper cites Pytorch image models.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Pytorch image models
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:07:43.853666Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7084467a-9fef-4a47-bb59-87d763ba3334 · outbound
This paper cites Arithmetic complexity of computations, volume 33.
Flopping for FLOPs: Leveraging equivariance for computational efficiency Arithmetic complexity of computations, volume 33
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.688679Z
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 1a1ad7bf-fee1-455b-87c4-9a4bfe52f16e · outbound
This paper cites and Shawe-Taylor , J.
Flopping for FLOPs: Leveraging equivariance for computational efficiency and Shawe-Taylor , J
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.679312Z
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 fbeda00b-6426-4c61-9ada-97aff7ca1fa8 · outbound
This paper cites e (2) -equivariant vision transformer.
Flopping for FLOPs: Leveraging equivariance for computational efficiency e (2) -equivariant vision transformer
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T20:07:44.669355Z
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 aaf5f6d8-83c4-4dea-8dc5-5cd509617c94 · outbound
This paper cites write newline.
Flopping for FLOPs: Leveraging equivariance for computational efficiency write newline
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T20:07:43.864760Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 8e1c8d9a-2852-4d21-93d2-cd2bc6839e88 · inbound
A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$ cites this paper.
A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$ Flopping for FLOPs: Leveraging equivariance for computational efficiency
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T20:03:56.489554Z
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 1a4739cb-a145-4506-a2aa-9a64926baf7f · inbound
A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$ cites this paper.
A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$ Flopping for FLOPs: Leveraging equivariance for computational efficiency
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T10:04:36.115425Z
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.