Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:26:06.966736Z
Paper Citation Record · LEDGER
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers
As of 16 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2505.07300.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
63 of 63 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 27a2c218-22df-47c0-9659-79845f245dbb · outbound
This paper cites Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas D.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas D
Reference 1
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Observation 927aa85f-a995-4e25-8d0e-1b848b5bf8df · outbound
This paper cites How does topology influence gradient propagation and model perfor- mance of deep networks with densenet-type skip connec- tions? In CVPR, 2021.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers How does topology influence gradient propagation and model perfor- mance of deep networks with densenet-type skip connec- tions? In CVPR, 2021
Reference 2
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Observation c13a70b4-ec0c-410b-aee6-a16b7dc58247 · outbound
This paper cites Low-rank bottleneck in multi-head attention models.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Low-rank bottleneck in multi-head attention models
Reference 3
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Observation 263f32f4-832b-4687-824b-caefd604b0cb · outbound
This paper cites Under- standing batch normalization.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Under- standing batch normalization
Reference 4
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Observation 304b0240-164d-4fc3-adb0-4d671b55a713 · outbound
This paper cites Proxylessnas: Direct neural architecture search on target task and hardware.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Proxylessnas: Direct neural architecture search on target task and hardware
Reference 5
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Observation 4ebffcd8-8def-4c40-936e-fdd17cc191b1 · outbound
This paper cites Once-for-all: Train one network and specialize it for efficient deployment.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Once-for-all: Train one network and specialize it for efficient deployment
Reference 6
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Observation 104e5013-7930-4e46-b00d-3e014e5035cb · outbound
This paper cites Fasterseg: Searching for faster real-time semantic segmentation.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Fasterseg: Searching for faster real-time semantic segmentation
Reference 7
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Observation 46d4e478-d847-4f0b-8ef4-d24016777296 · outbound
This paper cites Neural archi- tecture search on imagenet in four gpu hours: A theoretically inspired perspective.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Neural archi- tecture search on imagenet in four gpu hours: A theoretically inspired perspective
Reference 8
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Observation bd8e659f-42e5-4680-bf68-e7d4570076ba · outbound
This paper cites Progressive dif- ferentiable architecture search: Bridging the depth gap be- tween search and evaluation.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Progressive dif- ferentiable architecture search: Bridging the depth gap be- tween search and evaluation
Reference 9
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Observation 9918c4c6-88b1-4b3b-92ad-bd2ecfb76626 · outbound
This paper cites Autoformer: Searching transformers for visual recognition.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Autoformer: Searching transformers for visual recognition
Reference 10
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Observation 541ff423-d3ce-4607-8df6-a737bfcdcfd9 · outbound
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unresolved cited work
Reference 11
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Observation de8b4105-f5e3-4ade-a250-12085ba6557e · outbound
This paper cites Fairnas: Re- thinking evaluation fairness of weight sharing neural archi- tecture search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Fairnas: Re- thinking evaluation fairness of weight sharing neural archi- tecture search
Reference 12
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Observation 50c57cd3-3062-4d0b-b40b-aba8d0083643 · outbound
This paper cites Searching for a robust neural architecture in four gpu hours.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Searching for a robust neural architecture in four gpu hours
Reference 13
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Observation 5c752391-f990-4805-8f25-cf645e1496a8 · outbound
This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
Reference 14
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Source-reported events for the cited work
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Observation 0c21d13d-ff39-4062-8178-eecfe278b438 · outbound
This paper cites Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search
Reference 15
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Observation f7ac9e67-b06d-4a91-b52b-71cf6b3f351f · outbound
This paper cites Abdelfattah, Royson Lee, Hyeji Kim, and Nicholas D.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Abdelfattah, Royson Lee, Hyeji Kim, and Nicholas D
Reference 16
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Observation 198be63b-becf-47eb-bb3d-6d5bc9eef88e · outbound
This paper cites Neural architecture search: A survey.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Neural architecture search: A survey
Reference 17
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Observation dc98cb4f-629a-4830-846a-210d5253f1eb · outbound
This paper cites NASVit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers NASVit: Neural architecture search for efficient vision transformers with gradient conflict aware supernet training
Reference 18
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This paper cites Single path one-shot neural architecture search with uniform sampling.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Single path one-shot neural architecture search with uniform sampling
Reference 19
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Observation 669b216e-fd2f-4509-8fb8-814bc399b7de · outbound
This paper cites Gen- eralizable lightweight proxy for robust nas against diverse perturbations.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Gen- eralizable lightweight proxy for robust nas against diverse perturbations
Reference 20
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Observation 6066bf27-ba2b-45fa-b950-84746b1e1331 · outbound
This paper cites Complexity of linear re- gions in deep networks.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Complexity of linear re- gions in deep networks
Reference 21
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Observation a0bb52d3-c407-44e1-8ce3-4910316d877c · outbound
This paper cites Graph is all you need? lightweight data-agnostic neural architecture search without training, 2024.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Graph is all you need? lightweight data-agnostic neural architecture search without training, 2024
Reference 22
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Observation 8b6311a5-271d-49e9-afcd-d9a68f8406a3 · outbound
This paper cites An Introduction to Probability Theory.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers An Introduction to Probability Theory
Reference 23
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Observation 7cced468-7982-417f-8abc-dce7804c8e11 · outbound
This paper cites Neu- ral tangent kernel: Convergence and generalization in neural networks.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Neu- ral tangent kernel: Convergence and generalization in neural networks
Reference 24
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers NAS-bench-suite-zero: Accelerating research on zero cost proxies
Reference 25
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Az-nas: Assembling zero- cost proxies for network architecture search
Reference 26
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Observation 3d4779c1-08ca-4df0-b065-773b91d4ec3b · outbound
This paper cites Wide neural networks of any depth evolve as linear models under gradient descent.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Wide neural networks of any depth evolve as linear models under gradient descent
Reference 27
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This paper cites SNIP: Single-shot network pruning based on connection sen- sitivity.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers SNIP: Single-shot network pruning based on connection sen- sitivity
Reference 28
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Observation 077280ee-1788-4a88-b0b9-af18bfdc5a87 · outbound
This paper cites Zico: Zero-shot NAS via inverse coefficient of variation on gradients.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Zico: Zero-shot NAS via inverse coefficient of variation on gradients
Reference 29
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Observation 86efa896-0b22-4d93-af7f-a2a9ce74caf5 · outbound
This paper cites Zero-shot neu- ral architecture search: Challenges, solutions, and opportu- nities.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Zero-shot neu- ral architecture search: Challenges, solutions, and opportu- nities
Reference 30
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Observation 8f378d2c-fd2f-4f75-83d1-38037268ba87 · outbound
This paper cites Zen-nas: A zero-shot nas for high-performance image recognition.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Zen-nas: A zero-shot nas for high-performance image recognition
Reference 31
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Progressive neural architecture search
Reference 32
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers DARTS: Differentiable Architecture Search
Reference 33
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Reference 34
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Neural architecture optimization
Reference 35
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unresolved cited work
Reference 36
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This paper cites Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural ar- chitecture search without training? In CVPR, 2022.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Demystifying the neural tangent kernel from a practical perspective: Can it be trusted for neural ar- chitecture search without training? In CVPR, 2022
Reference 37
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This paper cites Evaluating efficient performance estimators of neural architectures.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Evaluating efficient performance estimators of neural architectures
Reference 38
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This paper cites Fayek, Vic Ciesiel- ski, and Xiaojun Chang.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Fayek, Vic Ciesiel- ski, and Xiaojun Chang
Reference 39
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
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This paper cites Efficient neural architecture search via parameter sharing.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Efficient neural architecture search via parameter sharing
Reference 40
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This paper cites On the expressive power of deep neural networks.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers On the expressive power of deep neural networks
Reference 41
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Large-scale evolution of image classifiers
Reference 42
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unresolved cited work
Reference 43
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Observation c0cd6686-3ed3-4008-b835-44e848686ac8 · outbound
This paper cites Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Reference 44
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Source-reported events for the cited work
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L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Vision transformer architecture search
Reference 45
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 12818fe1-c073-4148-bf99-84e21f014d58 · outbound
This paper cites Unleashing the power of gra- dient signal-to-noise ratio for zero-shot nas.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unleashing the power of gra- dient signal-to-noise ratio for zero-shot nas
Reference 46
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Observation 31aae708-362f-4c10-98d6-23b687e103ae · outbound
This paper cites Faster gaze prediction with dense networks and Fisher pruning.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Faster gaze prediction with dense networks and Fisher pruning
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:26:06.892272Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ccf3cafc-32ac-4a63-a15b-53c6841e48f6 · outbound
This paper cites Training data-efficient image transformers & distillation through at- tention.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Training data-efficient image transformers & distillation through at- tention
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.354899Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e280ea77-e2b3-46c0-9d5e-1053017a0dee · outbound
This paper cites an unresolved cited work.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unresolved cited work
Reference 49
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T22:26:07.339648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 759b83ff-8f94-4661-be1d-da453f921fa1 · outbound
This paper cites Neural Predictor for Neural Architecture Search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Neural Predictor for Neural Architecture Search
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:26:06.906109Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0c36a01-6653-48c1-9e57-a84e70046369 · outbound
This paper cites Fb- net: Hardware-aware efficient convnet design via differen- tiable neural architecture search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Fb- net: Hardware-aware efficient convnet design via differen- tiable neural architecture search
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.323701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 375f0c8b-ccfa-4264-ac2a-8b502dbb833c · outbound
This paper cites Exploiting network compress- ibility and topology in zero-cost NAS.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Exploiting network compress- ibility and topology in zero-cost NAS
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.307537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ec88c7ba-cfdf-4712-8f46-f24c452c4a4c · outbound
This paper cites Exploring randomly wired neural networks for im- age recognition.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Exploring randomly wired neural networks for im- age recognition
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.291746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation aa3a1776-93ee-45c5-87ea-f432b41b95a7 · outbound
This paper cites Snas: Stochastic neural architecture search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Snas: Stochastic neural architecture search
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.276110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e1e830df-1cf1-4a50-93c8-2b374fe999d8 · outbound
This paper cites On the number of linear regions of convolutional neural networks.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers On the number of linear regions of convolutional neural networks
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.262924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4d213bc6-06fd-47fd-8b70-28c9e068dfd1 · outbound
This paper cites PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:26:06.935360Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3807f821-3fc5-48e8-af41-5e9c1586036b · outbound
This paper cites Searching for BurgerFormer with micro-meso-macro space design.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Searching for BurgerFormer with micro-meso-macro space design
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.247757Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 920e8480-f0e1-450d-b045-1aadab1b2e82 · outbound
This paper cites Murphy, and Frank Hutter.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Murphy, and Frank Hutter
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.233462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3eb98684-9a1e-4925-a4d1-29c6b987eef6 · outbound
This paper cites Understanding and Robustifying Differentiable Architecture Search.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Understanding and Robustifying Differentiable Architecture Search
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:26:06.950078Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae978718-b87c-4a1f-8888-584d7c33e75c · outbound
This paper cites Surrogate nas bench- marks: Going beyond the limited search spaces of tabular nas benchmarks, 2022.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Surrogate nas bench- marks: Going beyond the limited search spaces of tabular nas benchmarks, 2022
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:26:06.954974Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a7ad532-6770-4050-8e2f-72ea676e4c95 · outbound
This paper cites GradSign: Model performance inference with theoretical insights.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers GradSign: Model performance inference with theoretical insights
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.208996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 079c1d67-be86-4652-a3f4-89cc7afc0814 · outbound
This paper cites Hytas: A hyperspectral image transformer archi- tecture search benchmark and analysis.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Hytas: A hyperspectral image transformer archi- tecture search benchmark and analysis
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T22:26:07.192902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e39e965f-d468-4248-9fb2-17151ce2e042 · outbound
This paper cites an unresolved cited work.
L-SWAG: Layer-Sample Wise Activation with Gradients information for Zero-Shot NAS on Vision Transformers Unresolved cited work
Reference 63
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T22:26:07.172474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.