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Path-conditioned training: a principled way to rescale ReLU neural networks

As of 9 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2602.19799.

A citation records a reference. It does not transfer a finding from one paper to another.

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2602.19799 v2

Coverage vector

measured 54 of 54 reference resolution

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measured 54 of 54 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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Outbound references

Observation c6501f07-4b57-4279-9f26-e8385a2500dd · outbound

This paper cites Natural gradient works efficiently in learning.

Path-conditioned training: a principled way to rescale ReLU neural networks Natural gradient works efficiently in learning

Reference 1

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Observation 2ef0a38c-9ca0-423a-9f4a-2b1d57c93518 · outbound

This paper cites Neural teleportation.

Path-conditioned training: a principled way to rescale ReLU neural networks Neural teleportation

Reference 2

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Observation 184e502b-a156-49ee-8756-f16e5b85f9fb · outbound

This paper cites Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks.

Path-conditioned training: a principled way to rescale ReLU neural networks Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks

Reference 3

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Observation fd9546f1-b148-4747-9403-67d36ef3a28a · outbound

This paper cites S., Woodworth, B.

Path-conditioned training: a principled way to rescale ReLU neural networks S., Woodworth, B

Reference 4

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Observation 4db07ae7-76b8-47fc-8fb6-361ae923dd5e · outbound

This paper cites Symmetry-invariant optimization in deep networks.

Path-conditioned training: a principled way to rescale ReLU neural networks Symmetry-invariant optimization in deep networks

Reference 5

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Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 6

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This paper cites Local identifiability of deep relu neural networks: the theory.

Path-conditioned training: a principled way to rescale ReLU neural networks Local identifiability of deep relu neural networks: the theory

Reference 7

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This paper cites A brief introduction to the neural tangent kernel.

Path-conditioned training: a principled way to rescale ReLU neural networks A brief introduction to the neural tangent kernel

Reference 8

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Observation 7784c279-3e43-4575-b7c0-480003e447d2 · outbound

This paper cites On lazy training in differentiable programming.

Path-conditioned training: a principled way to rescale ReLU neural networks On lazy training in differentiable programming

Reference 9

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This paper cites H., Leiserson, C.

Path-conditioned training: a principled way to rescale ReLU neural networks H., Leiserson, C

Reference 10

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This paper cites Natural neural networks.

Path-conditioned training: a principled way to rescale ReLU neural networks Natural neural networks

Reference 11

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Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 12

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This paper cites Sharp minima can generalize for deep nets.

Path-conditioned training: a principled way to rescale ReLU neural networks Sharp minima can generalize for deep nets

Reference 13

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Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 14

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Path-conditioned training: a principled way to rescale ReLU neural networks An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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Path-conditioned training: a principled way to rescale ReLU neural networks S., Hu, W., and Lee, J

Reference 16

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This paper cites On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

Path-conditioned training: a principled way to rescale ReLU neural networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 17

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This paper cites A Unified Paths Perspective for Pruning at Initialization.

Path-conditioned training: a principled way to rescale ReLU neural networks A Unified Paths Perspective for Pruning at Initialization

Reference 18

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This paper cites Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification.

Path-conditioned training: a principled way to rescale ReLU neural networks Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification

Reference 19

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Path-conditioned training: a principled way to rescale ReLU neural networks and Bengio, Y

Reference 20

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This paper cites Harnessing symmetries for modern deep learning challenges: a path-lifting perspective.

Path-conditioned training: a principled way to rescale ReLU neural networks Harnessing symmetries for modern deep learning challenges: a path-lifting perspective

Reference 21

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Path-conditioned training: a principled way to rescale ReLU neural networks A path-norm toolkit for modern networks: consequences, promises and challenges

Reference 22

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Path-conditioned training: a principled way to rescale ReLU neural networks Characterizing implicit bias in terms of optimization geometry

Reference 23

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Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 24

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Path-conditioned training: a principled way to rescale ReLU neural networks Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 25

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Path-conditioned training: a principled way to rescale ReLU neural networks Deep residual learning for image recognition

Reference 26

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Path-conditioned training: a principled way to rescale ReLU neural networks MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 27

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Path-conditioned training: a principled way to rescale ReLU neural networks Neural tangent kernel: Convergence and generalization in neural networks

Reference 28

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Path-conditioned training: a principled way to rescale ReLU neural networks Estimation with quadratic loss

Reference 29

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Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 30

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Path-conditioned training: a principled way to rescale ReLU neural networks A., and Dhillon, I

Reference 31

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Path-conditioned training: a principled way to rescale ReLU neural networks L., and Tanaka, H

Reference 32

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Path-conditioned training: a principled way to rescale ReLU neural networks Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning

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Path-conditioned training: a principled way to rescale ReLU neural networks Abide by the law and follow the flow: Conservation laws for gradient flows

Reference 34

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Path-conditioned training: a principled way to rescale ReLU neural networks Intrinsic training dynamics of deep neural networks

Reference 35

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Path-conditioned training: a principled way to rescale ReLU neural networks New insights and perspectives on the natural gradient method

Reference 36

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Path-conditioned training: a principled way to rescale ReLU neural networks G- SGD : Optimizing re LU neural networks in its positively scale-invariant space

Reference 37

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Path-conditioned training: a principled way to rescale ReLU neural networks and Burkholz, R

Reference 38

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Path-conditioned training: a principled way to rescale ReLU neural networks R., and Srebro, N

Reference 39

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source=arxiv_source observed=2026-08-02T21:38:26.134101Z digest=sha256:3eda80d8b74ada61f39f75203b908faf7ebb9d2ecee11a91b2c33c0afb28aaaf

Observation 49e5dc7f-5839-4a2a-9856-1db1ec9f4e1d · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Path-conditioned training: a principled way to rescale ReLU neural networks Pytorch: An imperative style, high-performance deep learning library

Reference 40

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source=arxiv_source observed=2026-08-02T21:38:26.240684Z digest=sha256:c09010b79c640d114995eceb66c192f77c83e822c8f3c57bcfa0207e8f0f621d

Observation 55300025-c3f0-4f33-88d6-5b2f6431c8ba · outbound

This paper cites an unresolved cited work.

Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 41

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source=arxiv_source observed=2026-08-02T21:38:26.324433Z digest=sha256:718496eee15082c6da40aa0a89de7cd6edb2dd550313efee50a3e8badc056a4e

Observation 627c721c-82db-4fb1-9610-bcb87f8130ec · outbound

This paper cites and Corvellec, M.

Path-conditioned training: a principled way to rescale ReLU neural networks and Corvellec, M

Reference 42

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source=arxiv_source observed=2026-08-02T21:38:26.428948Z digest=sha256:dbbea2a3d25b7433236634a2c69ef09e1d689f6de7ee2763d714685795cba7d4

Observation b087e936-ca84-49b9-bcaa-9fa7b9ff3fbf · outbound

This paper cites Computing Power and the Governance of Artificial Intelligence.

Path-conditioned training: a principled way to rescale ReLU neural networks Computing Power and the Governance of Artificial Intelligence

Reference 43

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source=arxiv_source observed=2026-08-02T21:38:26.492887Z digest=sha256:716fc7320e69edb4e01b8ca013607c5c06b146fd2e0f02006c4d736ae5280f1b

Observation 09ba9a9b-3c8a-40a4-88b9-e36d2b229c37 · outbound

This paper cites an unresolved cited work.

Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 44

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source=arxiv_source observed=2026-08-02T21:38:26.582065Z digest=sha256:7d039cfbce6091289872d996dcbf5806ae61ce8b6aec54b4fb33baf7ea2c846e

Observation 19dfe298-c99b-4b3d-9e38-0a16f7a7bee9 · outbound

This paper cites an unresolved cited work.

Path-conditioned training: a principled way to rescale ReLU neural networks Unresolved cited work

Reference 45

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source=arxiv_source observed=2026-08-02T21:38:26.680020Z digest=sha256:f5c6205c04c118f36fe84af5bd244532c74e606191e2b5e2e3e0e14c429b7cad

Observation 94bfc678-9e10-4edf-b5f6-76b6097f8f23 · outbound

This paper cites J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.

Path-conditioned training: a principled way to rescale ReLU neural networks J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al

Reference 46

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source=arxiv_source observed=2026-08-02T21:38:26.796022Z digest=sha256:59b45427d1135000c920b883f60a3c24343622f4911f2281583192549f9a036e

Observation 40e4488d-07d8-44ed-ad29-1b2ee4724475 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Path-conditioned training: a principled way to rescale ReLU neural networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 47

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source=arxiv_source observed=2026-08-02T21:38:26.921884Z digest=sha256:740ca4f9b31c91b44bd832015b4533e9bb242c47e870421321ea238d3f1b4092

Observation 85bab7d8-ced9-4ec5-a98d-053039d739ee · outbound

This paper cites and Gribonval, R.

Path-conditioned training: a principled way to rescale ReLU neural networks and Gribonval, R

Reference 48

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source=arxiv_source observed=2026-08-02T21:38:27.036217Z digest=sha256:bc184a660d416d9083e13854a5e10c033046639e206aab3f61f2dbe3ff28cd32

Observation d20cc988-bf93-4193-961c-9090581c4f93 · outbound

This paper cites Equi-normalization of neural networks, 2019.

Path-conditioned training: a principled way to rescale ReLU neural networks Equi-normalization of neural networks, 2019

Reference 49

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source=arxiv_source observed=2026-08-02T21:38:27.117250Z digest=sha256:13560a76bb3cdad28428c005715678449655c5b77f757409f992838e3287e73c

Observation 5e161e66-12a2-4460-b521-65c08aa50d67 · outbound

This paper cites Going deeper with convolutions.

Path-conditioned training: a principled way to rescale ReLU neural networks Going deeper with convolutions

Reference 50

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source=arxiv_source observed=2026-08-02T21:38:27.176982Z digest=sha256:165ca4240e0124966679751e88279ddff4519627658787f1b3cfc3b28ca5e75d

Observation 4be5399e-53b4-462a-a98b-ac0d1381b190 · outbound

This paper cites Growing tiny networks: Spotting expressivity bottlenecks and fixing them optimally.

Path-conditioned training: a principled way to rescale ReLU neural networks Growing tiny networks: Spotting expressivity bottlenecks and fixing them optimally

Reference 51

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source=arxiv_source observed=2026-08-02T21:38:27.249328Z digest=sha256:665ed50c16263f7d15b3111380f72683f05504924e748c30a19aa5c80e522e95

Observation 89a0a0f0-6c8e-4a49-bb5e-4668fcb962da · outbound

This paper cites Thermodynamik quantenmechanischer gesamtheiten.

Path-conditioned training: a principled way to rescale ReLU neural networks Thermodynamik quantenmechanischer gesamtheiten

Reference 52

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source=arxiv_source observed=2026-08-02T21:38:27.315521Z digest=sha256:69fa44906d04cdbd4d17ce9fc245dd9451070f4e70f06c61b63bdfa35892e885

Observation 6966831f-d5c6-4915-982b-09fce878dd71 · outbound

This paper cites Symmetry teleportation for accelerated optimization.

Path-conditioned training: a principled way to rescale ReLU neural networks Symmetry teleportation for accelerated optimization

Reference 53

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source=arxiv_source observed=2026-08-02T21:38:27.381657Z digest=sha256:edd9604ca2f6f29785e1891d24808ab8c484d6250257b9d0bf89c06074c6116b

Observation 7c9c8442-a459-4e32-ae80-3ab6f2f0cef3 · outbound

This paper cites Symmetries, flat minima, and the conserved quantities of gradient flow.

Path-conditioned training: a principled way to rescale ReLU neural networks Symmetries, flat minima, and the conserved quantities of gradient flow

Reference 54

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source=arxiv_source observed=2026-08-02T21:38:27.419250Z digest=sha256:8d6f70ef7fefbef0feed9fb54bd9daf800fe3fb254b9037f6e8548909392e905

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