NN-Former: Rethinking Graph Structure in Neural Architecture Representation | Paper Citation Record

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This paper cites Zero-Cost Proxies for Lightweight NAS.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Zero-Cost Proxies for Lightweight NAS

Reference 1

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This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 2

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This paper cites Contrastive neural archi- tecture search with neural architecture comparators.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Contrastive neural archi- tecture search with neural architecture comparators

Reference 3

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This paper cites Peephole: Predicting Network Performance Before Training.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Peephole: Predicting Network Performance Before Training

Reference 4

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Imagenet: A large-scale hierarchical image database

Reference 5

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This paper cites Repvgg: Making vgg-style convnets great again.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Repvgg: Making vgg-style convnets great again

Reference 6

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This paper cites ParZC: Parametric Zero-Cost Proxies for Efficient NAS.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation ParZC: Parametric Zero-Cost Proxies for Efficient NAS

Reference 7

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This paper cites NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 8

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This paper cites Pace: A parallelizable computation encoder for directed acyclic graphs.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Pace: A parallelizable computation encoder for directed acyclic graphs

Reference 9

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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This paper cites Brp-nas: Prediction-based nas using gcns.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Brp-nas: Prediction-based nas using gcns

Reference 11

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This paper cites A Generalization of Transformer Networks to Graphs.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation A Generalization of Transformer Networks to Graphs

Reference 12

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This paper cites Neural topological ordering for computation graphs.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Neural topological ordering for computation graphs

Reference 13

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This paper cites Neural message passing for quantum chemistry.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Neural message passing for quantum chemistry

Reference 14

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This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 15

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This paper cites Cmt: Convolutional neural networks meet vision transformers.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Cmt: Convolutional neural networks meet vision transformers

Reference 16

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This paper cites Inductive representation learning on large graphs.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Inductive representation learning on large graphs

Reference 17

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This paper cites Flowerformer: Empowering neural architecture encod- ing using a flow-aware graph transformer.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Flowerformer: Empowering neural architecture encod- ing using a flow-aware graph transformer

Reference 18

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This paper cites Cap: a context-aware neural predictor for nas.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Cap: a context-aware neural predictor for nas

Reference 19

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This paper cites Graph masked au- toencoder enhanced predictor for neural architecture search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Graph masked au- toencoder enhanced predictor for neural architecture search

Reference 20

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation A learned performance model for tensor processing units

Reference 21

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This paper cites Semi-supervised classi- fication with graph convolutional networks.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Semi-supervised classi- fication with graph convolutional networks

Reference 22

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This paper cites Answering complex queries in knowledge graphs with bidi- rectional sequence encoders.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Answering complex queries in knowledge graphs with bidi- rectional sequence encoders

Reference 23

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This paper cites Learning multiple layers of features from tiny images, 2009.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Learning multiple layers of features from tiny images, 2009

Reference 24

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This paper cites Neural graph em- bedding for neural architecture search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Neural graph em- bedding for neural architecture search

Reference 25

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Progressive neural architecture search

Reference 26

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This paper cites Nnlqp: A multi-platform neural network la- tency query and prediction system with an evolving database.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Nnlqp: A multi-platform neural network la- tency query and prediction system with an evolving database

Reference 27

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 28

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Decoupled Weight Decay Regularization

Reference 29

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This paper cites Tnasp: A transformer-based nas predictor with a self- evolution framework.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Tnasp: A transformer-based nas predictor with a self- evolution framework

Reference 30

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Pinat: A permutation invari- ance augmented transformer for nas predictor

Reference 31

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Neural architecture optimization

Reference 32

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Semi-supervised neural architecture search

Reference 33

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Transformers over directed acyclic graphs

Reference 34

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This paper cites A generic graph-based neural architecture encoding scheme for predictor-based nas.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation A generic graph-based neural architecture encoding scheme for predictor-based nas

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This paper cites Ta-gates: An encoding scheme for neu- ral network architectures.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Ta-gates: An encoding scheme for neu- ral network architectures

Reference 36

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Acceleration of stochastic approximation by averaging

Reference 37

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Estimates of the regression coefficient based on kendall’s tau

Reference 38

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This paper cites Bridging the gap between sample-based and one-shot neural architecture search with bonas.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Bridging the gap between sample-based and one-shot neural architecture search with bonas

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Going deeper with convolutions

Reference 40

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Directed Acyclic Graph Neural Networks

Reference 41

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Attention is all you need

Reference 42

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Graph at- tention networks

Reference 43

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Neural predictor for neural architecture search

Reference 44

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This paper cites Bananas: Bayesian optimization with neural architectures for neural architecture search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Bananas: Bayesian optimization with neural architectures for neural architecture search

Reference 45

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This paper cites Pay Less Attention with Lightweight and Dynamic Convolutions.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Pay Less Attention with Lightweight and Dynamic Convolutions

Reference 46

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This paper cites Representing long- range context for graph neural networks with global atten- tion.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Representing long- range context for graph neural networks with global atten- tion

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This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2018.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation How powerful are graph neural networks? In International Conference on Learning Representations, 2018

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NN-Former: Rethinking Graph Structure in Neural Architecture Representation Parcnetv2: Oversized kernel with enhanced attention

Reference 49

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This paper cites Renas: Relativistic eval- uation of neural architecture search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Renas: Relativistic eval- uation of neural architecture search

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Observation 148e74f5-445f-43e6-92be-6fa0e1218b35 · outbound

This paper cites Does unsupervised architecture representation learning help neural architecture search? Advances in Neural Information Processing Systems, 33:12486–12498, 2020.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Does unsupervised architecture representation learning help neural architecture search? Advances in Neural Information Processing Systems, 33:12486–12498, 2020

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This paper cites Nar-former: Neural architecture representation learning towards holistic attributes prediction.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Nar-former: Neural architecture representation learning towards holistic attributes prediction

Reference 52

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This paper cites Nar-former v2: Rethinking transformer for uni- versal neural network representation learning.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Nar-former v2: Rethinking transformer for uni- versal neural network representation learning

Reference 53

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Observation 7843c696-39ba-452a-87c4-c8ca67aea055 · outbound

This paper cites Nas-bench-101: Towards reproducible neural architecture search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Nas-bench-101: Towards reproducible neural architecture search

Reference 54

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This paper cites Do transformers really perform badly for graph representation? In Thirty-Fifth Conference on Neural Information Process- ing Systems, 2021.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Do transformers really perform badly for graph representation? In Thirty-Fifth Conference on Neural Information Process- ing Systems, 2021

Reference 55

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This paper cites Graph structure of neural networks.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Graph structure of neural networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:59.035958Z

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This paper cites Graph HyperNetworks for Neural Architecture Search.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Graph HyperNetworks for Neural Architecture Search

Reference 57

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This paper cites Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Nn-meter: Towards accurate latency prediction of deep-learning model inference on diverse edge devices

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:59.027576Z

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Observation ef5c0b52-85cb-4674-b597-47b8c2a618cb · outbound

This paper cites directed WL test.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation directed WL test

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:59.019251Z

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This paper cites For accuracy prediction, we show the experiment settings on NAS-Bench-101 in Section 2.1.1 and NAS- Bench-201 in Section 4.1.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation For accuracy prediction, we show the experiment settings on NAS-Bench-101 in Section 2.1.1 and NAS- Bench-201 in Section 4.1

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:59.011252Z

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This paper cites Ablation on hyperparameters This work adopts a Transformer as the backbone, and the hyperparameters of Transformers have been well-settled in previous research.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Ablation on hyperparameters This work adopts a Transformer as the backbone, and the hyperparameters of Transformers have been well-settled in previous research

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:59.003323Z

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Observation c701c7c7-dec7-4e04-9f94-4b46890f031d · outbound

This paper cites Theoretical Analysis Our ASMA method has less or equal computational com- plexity than the vanilla attention.

NN-Former: Rethinking Graph Structure in Neural Architecture Representation Theoretical Analysis Our ASMA method has less or equal computational com- plexity than the vanilla attention

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:11:58.995423Z

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