Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:25:00.131075Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 4 inbound Pith citation observations for arXiv:2505.15441.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:25:00.131075Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T12:27:30.763780Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T10:04:36.130406Z
69 of 69 outbound references displayed
External citation measurements
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Observation bbde78ef-2731-47fe-9e57-760ed0ae9bd1 · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance write newline
Reference 1
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Quick ViTs: Speeding up Vision Transformers through Equivariance @esa (Ref
Reference 2
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Observation 677ab94a-43ef-422d-84ed-e67708d7dd15 · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance Unresolved cited work
Reference 3
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Quick ViTs: Speeding up Vision Transformers through Equivariance Unresolved cited work
Reference 4
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Observation d9986c00-5005-46c0-b8b6-ab78941d8af3 · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance Accurate structure prediction of biomolecular interactions with alphafold 3
Reference 5
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Observation 5a7838e7-4130-458b-9f2f-3a220c078e16 · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance Getting vit in shape: Scaling laws for compute-optimal model design
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Quick ViTs: Speeding up Vision Transformers through Equivariance Vn-transformer: Rotation-equivariant attention for vector neurons
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Quick ViTs: Speeding up Vision Transformers through Equivariance How to scale your model
Reference 8
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Quick ViTs: Speeding up Vision Transformers through Equivariance Roto-translation covariant convolutional networks for medical image analysis
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Reference 10
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Observation 3bd2a9cf-6352-4f3a-a3ba-679f933ca5fa · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks
Reference 11
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Quick ViTs: Speeding up Vision Transformers through Equivariance o kman, David Nordstr \
Reference 12
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Quick ViTs: Speeding up Vision Transformers through Equivariance Does equivariance matter at scale? Transactions on Machine Learning Research, 2025
Reference 13
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Quick ViTs: Speeding up Vision Transformers through Equivariance Group-invariant max filtering
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Quick ViTs: Speeding up Vision Transformers through Equivariance End-to-end object detection with transformers
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Quick ViTs: Speeding up Vision Transformers through Equivariance Sparsevit: Revisiting activation sparsity for efficient high-resolution vision transformer
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Quick ViTs: Speeding up Vision Transformers through Equivariance Group equivariant convolutional networks
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Quick ViTs: Speeding up Vision Transformers through Equivariance Steerable CNN s
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Quick ViTs: Speeding up Vision Transformers through Equivariance Flash A ttention-2: Faster attention with better parallelism and work partitioning
Reference 19
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Quick ViTs: Speeding up Vision Transformers through Equivariance Cluster and predict latents patches for improved masked image modeling
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Reference 21
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Quick ViTs: Speeding up Vision Transformers through Equivariance Imagenet: A large-scale hierarchical image database
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Quick ViTs: Speeding up Vision Transformers through Equivariance Exploiting cyclic symmetry in convolutional neural networks
Reference 23
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Quick ViTs: Speeding up Vision Transformers through Equivariance An image is worth 16x16 words: Transformers for image recognition at scale
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Quick ViTs: Speeding up Vision Transformers through Equivariance RoMa: Robust Dense Feature Matching
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Quick ViTs: Speeding up Vision Transformers through Equivariance The pascal visual object classes (voc) challenge
Reference 26
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Quick ViTs: Speeding up Vision Transformers through Equivariance The invariantring package for macaulay2
Reference 27
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Quick ViTs: Speeding up Vision Transformers through Equivariance Fuchs, Daniel E
Reference 28
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Observation c7248ed7-e4dd-44d3-8c5f-700cbdfb709d · outbound
Quick ViTs: Speeding up Vision Transformers through Equivariance Grayson and Michael E
Reference 29
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Quick ViTs: Speeding up Vision Transformers through Equivariance Neighborhood attention transformer
Reference 30
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Quick ViTs: Speeding up Vision Transformers through Equivariance Efficient equivariant network
Reference 31
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Quick ViTs: Speeding up Vision Transformers through Equivariance Gaussian Error Linear Units (GELUs)
Reference 32
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Quick ViTs: Speeding up Vision Transformers through Equivariance Lietransformer: Equivariant self-attention for lie groups
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Quick ViTs: Speeding up Vision Transformers through Equivariance Equivariance with learned canonicalization functions
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Quick ViTs: Speeding up Vision Transformers through Equivariance The bispectrum as a source of phase-sensitive invariants for fourier descriptors: a group-theoretic approach
Reference 35
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Quick ViTs: Speeding up Vision Transformers through Equivariance Scaling Laws for Neural Language Models
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Quick ViTs: Speeding up Vision Transformers through Equivariance Berg, Wan-Yen Lo, Piotr Dollar, and Ross Girshick
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Quick ViTs: Speeding up Vision Transformers through Equivariance Dinobloom: a foundation model for generalizable cell embeddings in hematology
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Quick ViTs: Speeding up Vision Transformers through Equivariance Steerable transformers for volumetric data
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Quick ViTs: Speeding up Vision Transformers through Equivariance Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
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Quick ViTs: Speeding up Vision Transformers through Equivariance Swin transformer: Hierarchical vision transformer using shifted windows
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Quick ViTs: Speeding up Vision Transformers through Equivariance An expert-annotated dataset of bone marrow cytology in hematologic malignancies
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Quick ViTs: Speeding up Vision Transformers through Equivariance cuEquivariance : High-performance equivariant neural networks
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Quick ViTs: Speeding up Vision Transformers through Equivariance Pytorch: An imperative style, high-performance deep learning library
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Quick ViTs: Speeding up Vision Transformers through Equivariance Rojas-Gomez, Teck-Yian Lim, Minh N
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Quick ViTs: Speeding up Vision Transformers through Equivariance Attentive group equivariant convolutional networks
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Quick ViTs: Speeding up Vision Transformers through Equivariance A general framework for robust g-invariance in g-equivariant networks
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Quick ViTs: Speeding up Vision Transformers through Equivariance Deit iii: Revenge of the vit
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Quick ViTs: Speeding up Vision Transformers through Equivariance Probing Equivariance and Symmetry Breaking in Convolutional Networks
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Quick ViTs: Speeding up Vision Transformers through Equivariance Benchmarking representation learning for natural world image collections
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Quick ViTs: Speeding up Vision Transformers through Equivariance Attention is all you need
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