Paper Citation Record · LEDGER

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models

As of 13 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2411.17182.

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

pith.paper-citation-record.v1
2411.17182 v1

Coverage vector

measured 48 of 48 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 8ac51476-fb4a-47f2-b0f9-cd2b04cc9ae8 · outbound

This paper cites Repulsive attention: Rethinking multi-head attention as bayesian inference.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Repulsive attention: Rethinking multi-head attention as bayesian inference

Reference 1

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This paper cites Layer Normalization.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Layer Normalization

Reference 2

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This paper cites Spectrally-normalized margin bounds for neural networks.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Spectrally-normalized margin bounds for neural networks

Reference 3

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This paper cites Birth of a transformer: A memory viewpoint.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Birth of a transformer: A memory viewpoint

Reference 4

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This paper cites Attention approximates sparse distributed memory.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Attention approximates sparse distributed memory

Reference 5

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This paper cites Invariant scattering convolution networks.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Invariant scattering convolution networks

Reference 6

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Observation 3e9edc9b-69b6-42fd-ada6-314db41fb187 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Emerging properties in self-supervised vision transformers

Reference 7

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This paper cites Transformer interpretability beyond attention visualization.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Transformer interpretability beyond attention visualization

Reference 8

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This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Randaugment: Practical automated data augmentation with a reduced search space

Reference 9

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This paper cites Analyzing transformers in embedding space.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Analyzing transformers in embedding space

Reference 10

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

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

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This paper cites A mathematical framework for transformer circuits.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models A mathematical framework for transformer circuits

Reference 12

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This paper cites The emergence of clusters in self-attention dynamics.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models The emergence of clusters in self-attention dynamics

Reference 13

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Observation e79ac14f-aa14-437b-bf24-6cd0f9b3ac1f · outbound

This paper cites Patchscopes: A unifying framework for inspecting hidden representations of language models.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Patchscopes: A unifying framework for inspecting hidden representations of language models

Reference 14

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This paper cites Learning fast approximations of sparse coding.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Learning fast approximations of sparse coding

Reference 15

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This paper cites The Forward-Forward Algorithm: Some Preliminary Investigations.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models The Forward-Forward Algorithm: Some Preliminary Investigations

Reference 16

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This paper cites Energy transformer.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Energy transformer

Reference 17

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This paper cites Fantastic generalization measures and where to find them.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Fantastic generalization measures and where to find them

Reference 18

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This paper cites A new measure of rank correlation.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models A new measure of rank correlation

Reference 19

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This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models On large-batch training for deep learning: Generalization gap and sharp minima

Reference 20

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Kingma and Jimmy Ba

Reference 21

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Tracr: Compiled transformers as a laboratory for interpretability

Reference 22

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This paper cites Omnigrok: Grokking beyond algorithmic data.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Omnigrok: Grokking beyond algorithmic data

Reference 23

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This paper cites Segmentation of multivariate mixed data via lossy data coding and compression.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Segmentation of multivariate mixed data via lossy data coding and compression

Reference 24

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Pac-bayesian model averaging

Reference 25

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Universal hopfield networks: A general framework for single-shot associative memory models

Reference 26

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Progress measures for grokking via mechanistic interpretability

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Exploring generalization in deep learning

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This paper cites A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks

Reference 30

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This paper cites Path-sgd: Path-normalized optimization in deep neural networks.

An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Path-sgd: Path-normalized optimization in deep neural networks

Reference 31

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Norm-based capacity control in neural networks

Reference 32

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Theoretical foundations of deep learning via sparse representations: A multilayer sparse model and its connection to convolutional neural networks

Reference 33

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Hopfield networks is all you need

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Unraveling attention via convex duality: Analysis and interpretations of vision transformers

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Biological learning in key-value memory networks

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models On the uniform convergence of relative frequencies of events to their probabilities

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Attention is all you need

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Interpretability in the wild: a circuit for indirect object identification in GPT-2 small

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Thinking like transformers

Reference 41

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Graph neural networks inspired by classical iterative algorithms

Reference 42

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Transformers from an optimization perspective

Reference 43

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Attentionviz: A global view of transformer attention

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models White-box transformers via sparse rate reduction

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Learning diverse and discriminative representations via the principle of maximal coding rate reduction

Reference 46

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Unveiling Transformers with LEGO: a synthetic reasoning task

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An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models Unresolved cited work

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Pith citing papers

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