Paper Citation Record · LEDGER

Low-latency vision transformers via large-scale multi-head attention

As of 7 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.23832.

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

pith.paper-citation-record.v1
2506.23832 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:39:37.185815Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:01:20.394734Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T11:01:21.358336Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 9e89b1b9-4f80-4f72-8cbd-32ca8fce7d45 · outbound

This paper cites an unresolved cited work.

Low-latency vision transformers via large-scale multi-head attention Unresolved cited work

Reference 1

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Low-latency vision transformers via large-scale multi-head attention Unresolved cited work

Reference 2

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Low-latency vision transformers via large-scale multi-head attention Unresolved cited work

Reference 3

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Observation 38ad5206-51e2-48e2-bb9a-9138dfadee25 · outbound

This paper cites Several CCT architectures comprising of seven components , with varying numbers of CLs, transformer blocks, heads, and 𝑑𝑖𝑚, achieve similar accuracies of ~0.81 (Tables 2–4).

Low-latency vision transformers via large-scale multi-head attention Several CCT architectures comprising of seven components , with varying numbers of CLs, transformer blocks, heads, and 𝑑𝑖𝑚, achieve similar accuracies of ~0.81 (Tables 2–4)

Reference 4

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This paper cites ignition.

Low-latency vision transformers via large-scale multi-head attention ignition

Reference 5

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Reference 6

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

Low-latency vision transformers via large-scale multi-head attention Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 7

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Low-latency vision transformers via large-scale multi-head attention Unresolved cited work

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Low-latency vision transformers via large-scale multi-head attention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation a6773015-0e46-4557-aba7-a21cb3c1723c · outbound

This paper cites Vaswani, Attention is all you need, Advances in Neural Information Processing Systems, (2017).

Low-latency vision transformers via large-scale multi-head attention Vaswani, Attention is all you need, Advances in Neural Information Processing Systems, (2017)

Reference 10

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Observation 0fc6cdd0-6d73-4d66-a852-b0e85994817c · outbound

This paper cites On the Relationship between Self-Attention and Convolutional Layers.

Low-latency vision transformers via large-scale multi-head attention On the Relationship between Self-Attention and Convolutional Layers

Reference 11

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Observation 3f7bdc5f-f13b-43c8-849b-ac96f973a776 · outbound

This paper cites Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi.

Low-latency vision transformers via large-scale multi-head attention Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi

Reference 13

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This paper cites Advanced deep architecture pruning using single filter performance.

Low-latency vision transformers via large-scale multi-head attention Advanced deep architecture pruning using single filter performance

Reference 14

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This paper cites Barra, G.

Low-latency vision transformers via large-scale multi-head attention Barra, G

Reference 15

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This paper cites Agliari, L.

Low-latency vision transformers via large-scale multi-head attention Agliari, L

Reference 16

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Observation 98c62ac3-a01d-44a5-9e82-6044d732fc29 · outbound

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

Low-latency vision transformers via large-scale multi-head attention Escaping the Big Data Paradigm with Compact Transformers

Reference 17

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This paper cites Krizhevsky, G.

Low-latency vision transformers via large-scale multi-head attention Krizhevsky, G

Reference 18

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Low-latency vision transformers via large-scale multi-head attention Koresh, T

Reference 19

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Low-latency vision transformers via large-scale multi-head attention Tevet, R.D

Reference 20

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Low-latency vision transformers via large-scale multi-head attention The mechanism underlying successful deep learning

Reference 23

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Low-latency vision transformers via large-scale multi-head attention Breiman, Bagging predictors, Machine learning, 24 (1996) 123-140

Reference 24

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Low-latency vision transformers via large-scale multi-head attention Kumari, D

Reference 25

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Low-latency vision transformers via large-scale multi-head attention Nakkiran, G

Reference 26

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Low-latency vision transformers via large-scale multi-head attention Reformer: The Efficient Transformer

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Low-latency vision transformers via large-scale multi-head attention Blatt, E

Reference 31

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Low-latency vision transformers via large-scale multi-head attention RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 34

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Low-latency vision transformers via large-scale multi-head attention GPT-4 Technical Report

Reference 35

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Low-latency vision transformers via large-scale multi-head attention A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?

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Low-latency vision transformers via large-scale multi-head attention dreaming

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Low-latency vision transformers via large-scale multi-head attention Agliari, F

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Low-latency vision transformers via large-scale multi-head attention Salakhutdinov, G

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This paper cites Ivanov, The new field of network physiology: building the human physiolome, in, Frontiers Media SA, 2021, pp.

Low-latency vision transformers via large-scale multi-head attention Ivanov, The new field of network physiology: building the human physiolome, in, Frontiers Media SA, 2021, pp

Reference 40

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Low-latency vision transformers via large-scale multi-head attention Ivanov, R.P

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Low-latency vision transformers via large-scale multi-head attention Ivanov, K.K

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Low-latency vision transformers via large-scale multi-head attention mixup: Beyond Empirical Risk Minimization

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Low-latency vision transformers via large-scale multi-head attention Cubuk, B

Reference 45

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Low-latency vision transformers via large-scale multi-head attention Zhong, L

Reference 46

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Low-latency vision transformers via large-scale multi-head attention L2 Regularization for Learning Kernels

Reference 47

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Low-latency vision transformers via large-scale multi-head attention SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 48

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Low-latency vision transformers via large-scale multi-head attention ResNet strikes back: An improved training procedure in timm

Reference 49

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Low-latency vision transformers via large-scale multi-head attention Schmidhuber, Deep learning in neural networks: An overview, Neural networks, 61 (2015) 85-117

Reference 50

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Low-latency vision transformers via large-scale multi-head attention Llugsi, S

Reference 51

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Pith citing papers

Observation 15c01bdf-e0a9-440b-a0c5-a8f1d4c37ffb · inbound

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning cites this paper.

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning Low-latency vision transformers via large-scale multi-head attention

Reference 20

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