Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights | Paper Citation Record

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This paper cites Zero-Cost Proxies for Lightweight NAS.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Zero-Cost Proxies for Lightweight NAS

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This paper cites Learning to ran k using gradient descent.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Learning to ran k using gradient descent

Reference 2

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This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Reference 3

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

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Once-for-All: Train One Network and Specialize it for Efficient Deployment

Reference 4

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This paper cites BN-NAS: Neural architecture search with batch normalization.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights BN-NAS: Neural architecture search with batch normalization

Reference 5

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This paper cites Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Reference 6

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This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 7

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This paper cites Mathematical methods of statistics.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Mathematical methods of statistics

Reference 8

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This paper cites Imagenet: A large-scale hierarchical image datab ase.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Imagenet: A large-scale hierarchical image datab ase

Reference 9

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

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search

Reference 10

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This paper cites Exploratory data analysis using Fisher information.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Exploratory data analysis using Fisher information

Reference 11

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This paper cites Complexity of linear reg ions in deep networks.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Complexity of linear reg ions in deep networks

Reference 12

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This paper cites Ne ural tan- gent kernel: Convergence and generalization in neural netw orks.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Ne ural tan- gent kernel: Convergence and generalization in neural netw orks

Reference 13

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This paper cites Surprisingly Strong Performance Prediction with Neural Graph Features.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Surprisingly Strong Performance Prediction with Neural Graph Features

Reference 14

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This paper cites Pathological spectra of the Fisher information metric and its variants in deep neural networks.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Pathological spectra of the Fisher information metric and its variants in deep neural networks

Reference 15

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This paper cites Univ ersal statistics of ﬁsher information in deep neural networks: Me an ﬁeld approach.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Univ ersal statistics of ﬁsher information in deep neural networks: Me an ﬁeld approach

Reference 16

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This paper cites A new measure of rank correlation.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights A new measure of rank correlation

Reference 17

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This paper cites Learning mult iple layers of features from tiny images.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Learning mult iple layers of features from tiny images

Reference 18

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This paper cites L imita- tions of the empirical ﬁsher approximation for natural grad ient descent.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights L imita- tions of the empirical ﬁsher approximation for natural grad ient descent

Reference 19

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This paper cites Masking adversarial damage: Finding adversarial saliency for robu st and sparse network.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Masking adversarial damage: Finding adversarial saliency for robu st and sparse network

Reference 20

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This paper cites AZ-NAS: Assembling zero- cost proxies for network architecture search.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights AZ-NAS: Assembling zero- cost proxies for network architecture search

Reference 21

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This paper cites Wide neural networks of any depth evolve as linear models under gradient descent.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Wide neural networks of any depth evolve as linear models under gradient descent

Reference 22

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This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 23

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This paper cites Linear algebra with applications.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Linear algebra with applications

Reference 24

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This paper cites ZiCo: Zero-shot NAS via inverse coefﬁcient of variation on gradients.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights ZiCo: Zero-shot NAS via inverse coefﬁcient of variation on gradients

Reference 25

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This paper cites Zen-nas: A zero-shot nas for high-performance image recognition.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Zen-nas: A zero-shot nas for high-performance image recognition

Reference 26

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Observation f9ba9db3-a52e-4f80-834f-cc536d708114 · outbound

This paper cites DARTS: Differentiable Architecture Search.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights DARTS: Differentiable Architecture Search

Reference 27

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This paper cites A tutorial on ﬁsher information.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights A tutorial on ﬁsher information

Reference 28

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This paper cites New insights and perspectives on the nat ural gradient method.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights New insights and perspectives on the nat ural gradient method

Reference 29

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This paper cites Nas-bench-asr: Reproducible neural architecture search for speech recognition.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Nas-bench-asr: Reproducible neural architecture search for speech recognition

Reference 30

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This paper cites Neural architecture search without training.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Neural architecture search without training

Reference 31

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This paper cites Distil ling optimal neural networks: Rapid search in diverse spaces.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Distil ling optimal neural networks: Rapid search in diverse spaces

Reference 32

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This paper cites Evaluating efﬁ- cient performance estimators of neural architectures.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Evaluating efﬁ- cient performance estimators of neural architectures

Reference 33

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This paper cites Fast ﬁnite width neural tangent kernel.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Fast ﬁnite width neural tangent kernel

Reference 34

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This paper cites Adaptive natural gradi ent method for learning of stochastic neural networks in mini-b atch mode.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Adaptive natural gradi ent method for learning of stochastic neural networks in mini-b atch mode

Reference 35

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Observation 706df0b3-ffb4-4dc2-89bb-a2b3ee51adbf · outbound

This paper cites The spectrum of th e ﬁsher information matrix of a single-hidden-layer neural networ k.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights The spectrum of th e ﬁsher information matrix of a single-hidden-layer neural networ k

Reference 36

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This paper cites Information and the accuracy attai nable in the estimation of statistical parameters.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Information and the accuracy attai nable in the estimation of statistical parameters

Reference 37

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This paper cites Mobilenetv2: Inverted resi d- uals and linear bottlenecks.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Mobilenetv2: Inverted resi d- uals and linear bottlenecks

Reference 38

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Observation d015df08-06b5-4ac3-990f-65bd4b9f3be2 · outbound

This paper cites The proof and measurement of associa tion between two things.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights The proof and measurement of associa tion between two things

Reference 39

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This paper cites An exact cholesky deco m- position and the generalized inverse of the variance–covar iance matrix of the multinomial distribution, with applications.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights An exact cholesky deco m- position and the generalized inverse of the variance–covar iance matrix of the multinomial distribution, with applications

Reference 40

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This paper cites Pruning neural networks without any data by itera- tively conserving synaptic ﬂow.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Pruning neural networks without any data by itera- tively conserving synaptic ﬂow

Reference 41

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This paper cites Picking Winning Tickets Before Training by Preserving Gradient Flow.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 42

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This paper cites A deeper look at zero-cost proxies for lightweight NAS.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights A deeper look at zero-cost proxies for lightweight NAS

Reference 43

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Observation ccb1ba4c-e86b-4de1-af63-2b4f38723f74 · outbound

This paper cites On the number of linear regions of convolutional neural networks.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights On the number of linear regions of convolutional neural networks

Reference 44

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Observation 4e58cc95-24aa-4a14-9c34-2f3f07575b9a · outbound

This paper cites CARS: Continuous evolution for efﬁcient neural architecture search.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights CARS: Continuous evolution for efﬁcient neural architecture search

Reference 45

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Observation 3ccf8196-8e4d-4178-9e5e-53a02cec6214 · outbound

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

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Nas-bench-101: Towards repro- ducible neural architecture search

Reference 46

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Observation 51a6e3ec-73ab-4c8f-bd1a-2d0b5032b7d4 · outbound

This paper cites A theoretical analysis of ndcg ranking measure s.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights A theoretical analysis of ndcg ranking measure s

Reference 47

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Observation 8253b640-e1f2-4d27-9fd2-98d0591ad8c4 · outbound

This paper cites Neural architecture search with random labels.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Neural architecture search with random labels

Reference 48

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Observation cb73891c-862e-4ad3-85ef-ccface29297e · outbound

This paper cites GradSign: Model Performance Inference with Theoretical Insights.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights GradSign: Model Performance Inference with Theoretical Insights

Reference 49

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This paper cites Learning transferable architectures for scalable imag e recognition.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Learning transferable architectures for scalable imag e recognition

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:49:36.421423Z

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Observation 98728326-c304-4363-96e1-c444f8f99c1c · outbound

This paper cites Our VKDNW proxy has the lowest correlation, ie.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Our VKDNW proxy has the lowest correlation, ie

Reference 51

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