Paper Citation Record · LEDGER

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2607.10593.

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

pith.paper-citation-record.v1
2607.10593 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T10:36:03.467256Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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verified fuzzy0
unresolved37
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad7ba017-2600-486a-86b8-284a8c3f3b93 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 1

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Observation 1977531c-4fa5-4e53-a814-5e4a121ac63f · outbound

This paper cites Layer Normalization.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Layer Normalization

Reference 2

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Observation ea814105-7f13-43f9-b8c0-bb592f0f2cca · outbound

This paper cites Attention is all you need,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Attention is all you need,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation a9df3cdc-c1d3-43aa-9987-06bf2b634104 · outbound

This paper cites Categorical reparameterization with Gumbel-Softmax,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Categorical reparameterization with Gumbel-Softmax,

Reference 4

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Observation bb37de26-d566-4a95-8519-b06c9ee5f16f · outbound

This paper cites Adam: A method for stochastic optimization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Adam: A method for stochastic optimization,

Reference 5

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Observation 9c488932-bd52-490a-b10f-d04e4964167d · outbound

This paper cites Decoupled weight decay regularization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Decoupled weight decay regularization,

Reference 6

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Observation 1fc35617-7584-496d-999e-1d80b6d28a54 · outbound

This paper cites On layer normalization in the transformer architecture,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating On layer normalization in the transformer architecture,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 15e7fd4d-20b5-4f4f-924e-49de83a1eedb · outbound

This paper cites Benchmarking neural network robust- ness to common corruptions and perturbations,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Benchmarking neural network robust- ness to common corruptions and perturbations,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 83bed5a6-8b6a-406b-a1a5-7e9fa8615a09 · outbound

This paper cites Fixup initialization: Residual learning without normal- ization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Fixup initialization: Residual learning without normal- ization,

Reference 9

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Observation dd301e37-b835-4eed-9729-f211585111b6 · outbound

This paper cites In search of lost domain generalization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating In search of lost domain generalization,

Reference 10

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Observation a4d183a4-8ee8-429c-ace9-e6645bb6ad14 · outbound

This paper cites DARTS: Differentiable architecture search,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating DARTS: Differentiable architecture search,

Reference 11

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Observation 29795267-fccc-49fc-94cc-1666b2629224 · outbound

This paper cites Neural architecture search with reinforcement learning,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Neural architecture search with reinforcement learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 6d277e3b-9cc5-48e6-abab-32530f3bba22 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 1ac2bb0c-60f2-4a45-87d3-9eef3ca4b8f8 · outbound

This paper cites Group normalization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Group normalization,

Reference 14

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Observation b7acb265-2653-49a3-9718-0ac4be3f8712 · outbound

This paper cites Adaptive batch normalization for practical domain adapta- tion,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Adaptive batch normalization for practical domain adapta- tion,

Reference 15

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Observation a5f73626-9260-4004-bab3-cfcd8552fb0b · outbound

This paper cites DeepNet: Scaling Transformers to 1,000 Layers.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating DeepNet: Scaling Transformers to 1,000 Layers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 608a5f0f-5303-456d-aef4-64a9b3611094 · outbound

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

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,

Reference 17

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Observation a988d1af-9179-4855-a36c-bbf2abab56c1 · outbound

This paper cites Searching for Activation Functions.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Searching for Activation Functions

Reference 18

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Observation e0d2c989-d995-4523-a567-03e670c16a90 · outbound

This paper cites Activate or not: Learning customized activation,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Activate or not: Learning customized activation,

Reference 19

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Observation 8458c5a2-a322-4274-8f88-a9df40485e89 · outbound

This paper cites Squeeze-and-excitation networks,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Squeeze-and-excitation networks,

Reference 20

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Observation 2a9df2a7-cd30-4dce-a1b7-6c86493b0c09 · outbound

This paper cites Dynamic convolution: Attention over convolution kernels,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Dynamic convolution: Attention over convolution kernels,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 1736251f-8a51-4e8f-8de6-171c454ac425 · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Adaptive subgradient methods for online learning and stochastic optimization,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 69d34855-7d85-4688-af9d-593060a275c5 · outbound

This paper cites AdaBelief optimizer: Adapting stepsizes by the belief in observed gradients,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating AdaBelief optimizer: Adapting stepsizes by the belief in observed gradients,

Reference 23

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Observation f4fe2f35-a3ea-42c4-849e-78601196b7fa · outbound

This paper cites Root mean square layer normalization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Root mean square layer normalization,

Reference 24

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Observation 247fe0f6-be54-41ee-9a65-ff2d619d5b81 · outbound

This paper cites ReZero is all you need: Fast convergence at large depth,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating ReZero is all you need: Fast convergence at large depth,

Reference 25

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Observation 1c90b444-1f5b-4b6b-bfe2-4f1ab21f905e · outbound

This paper cites Mixup: Beyond empirical risk minimization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Mixup: Beyond empirical risk minimization,

Reference 26

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Observation 878ec45b-59c6-477d-94fd-61256f4e2851 · outbound

This paper cites CutMix: Regularization strategy to train strong classifiers with localizable features,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating CutMix: Regularization strategy to train strong classifiers with localizable features,

Reference 27

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Observation ccbeb99e-2d7d-477c-98ea-507dabe0baaa · outbound

This paper cites Distilling the Knowledge in a Neural Network.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Distilling the Knowledge in a Neural Network

Reference 28

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Observation ecf4811b-00aa-48bb-91ec-dddb2150f090 · outbound

This paper cites Outrageously large neural networks: The mixture-of- experts layer,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Outrageously large neural networks: The mixture-of- experts layer,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

Source-reported events for the cited work

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Observation f3092301-5d49-4e3d-b339-9f508897ca38 · outbound

This paper cites Transformers without normalization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Transformers without normalization,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 38904f77-3772-45fb-a367-4c33c738596a · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating FiLM: Visual reasoning with a general conditioning layer,

Reference 31

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Observation 0be5c44d-127c-4cba-be13-54d86c005763 · outbound

This paper cites Escaping the Big Data Paradigm with Compact Transformers.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Escaping the Big Data Paradigm with Compact Transformers

Reference 32

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Observation 582581c6-94f5-4621-9ae5-f6e129b35906 · outbound

This paper cites Building a large annotated corpus of English: The Penn Treebank,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Building a large annotated corpus of English: The Penn Treebank,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 99d2c105-1527-4fcd-89a0-06fec2f9c75a · outbound

This paper cites Differentiable learning- to-normalize via switchable normalization,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Differentiable learning- to-normalize via switchable normalization,

Reference 34

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Observation 0796bb42-5d71-4fe7-aec8-5da9a8b2cff5 · outbound

This paper cites Deep networks with stochastic depth,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Deep networks with stochastic depth,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation 71074e04-1332-4da4-bf24-4b51f2b3cbf3 · outbound

This paper cites Going deeper with image transformers,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Going deeper with image transformers,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation b4c82f2b-6c57-46df-bc2b-7664954a3486 · outbound

This paper cites Transformer-XL: Attentive language models beyond a fixed-length context,.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Transformer-XL: Attentive language models beyond a fixed-length context,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Pith citing papers

No inbound Pith citation observations are available.