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Paper Citation Record · LEDGER

Neuroscience-inspired online unsupervised learning algorithms

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:1908.01867.

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

pith.paper-citation-record.v1
1908.01867 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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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

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation c1c939c3-2514-441a-8cee-750842f283a1 · outbound

This paper cites The perceptron: a probabilistic model for information storage and organization in the brain.

Neuroscience-inspired online unsupervised learning algorithms The perceptron: a probabilistic model for information storage and organization in the brain

Reference 1

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Observation 3b1318b4-8b4f-4938-b5ce-dcbe7a2d0c44 · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning,.

Neuroscience-inspired online unsupervised learning algorithms Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 2

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Observation f38788ba-af5c-4ac8-8ae6-cdab30572ba1 · outbound

This paper cites A mixed-mode array computing architecture for online dictio- nary learning,.

Neuroscience-inspired online unsupervised learning algorithms A mixed-mode array computing architecture for online dictio- nary learning,

Reference 3

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Observation b7fff841-8242-4040-874c-8d0ebfde911a · outbound

This paper cites Emergence of simple-cell receptive field properties by learning a sparse code for natural images,.

Neuroscience-inspired online unsupervised learning algorithms Emergence of simple-cell receptive field properties by learning a sparse code for natural images,

Reference 4

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Observation 474ef9af-7763-4b7e-b6d1-fbca7db8a6ab · outbound

This paper cites Simplified neuron model as a principal component analyzer,.

Neuroscience-inspired online unsupervised learning algorithms Simplified neuron model as a principal component analyzer,

Reference 5

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Observation 5b863663-a298-4069-8eb7-7f7d5b67650a · outbound

This paper cites Generalized low rank models,.

Neuroscience-inspired online unsupervised learning algorithms Generalized low rank models,

Reference 6

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Observation 36a042bd-1c6e-46e2-8d39-831f31db36f4 · outbound

This paper cites A normative theory of adaptive dimensionality reduction in neural networks,.

Neuroscience-inspired online unsupervised learning algorithms A normative theory of adaptive dimensionality reduction in neural networks,

Reference 7

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Observation 8965bdcc-de3f-49a3-b9da-58c59456ec8d · outbound

This paper cites Stochastic optimization for pca and pls,.

Neuroscience-inspired online unsupervised learning algorithms Stochastic optimization for pca and pls,

Reference 8

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Observation 10ce2423-61da-4918-907f-0a68eb64a1ee · outbound

This paper cites Candid covariance-free incremental principal compo- nent analysis,.

Neuroscience-inspired online unsupervised learning algorithms Candid covariance-free incremental principal compo- nent analysis,

Reference 9

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Observation 9c1f2af5-cf1c-42a2-8820-053c2355a0ac · outbound

This paper cites Efficient principal subspace projection of streaming data through fast similarity matching,.

Neuroscience-inspired online unsupervised learning algorithms Efficient principal subspace projection of streaming data through fast similarity matching,

Reference 10

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Observation 720534e6-fc61-4c17-af2e-139dd4f35d7d · outbound

This paper cites Learning the parts of objects by non-negative matrix factoriza- tion,.

Neuroscience-inspired online unsupervised learning algorithms Learning the parts of objects by non-negative matrix factoriza- tion,

Reference 11

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Observation 8436befc-4a36-4678-8e4b-b2b89465cc9c · outbound

This paper cites Adaptive network for optimal linear feature extraction,.

Neuroscience-inspired online unsupervised learning algorithms Adaptive network for optimal linear feature extraction,

Reference 12

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Neuroscience-inspired online unsupervised learning algorithms Unresolved cited work

Reference 13

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Neuroscience-inspired online unsupervised learning algorithms Sparse coding with an overcomplete basis set: A strategy employed by v1?

Reference 14

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Observation b1957c69-6c35-4d08-b0f5-7d6235b57c46 · outbound

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Neuroscience-inspired online unsupervised learning algorithms Matrix completion has no spurious local minimum,

Reference 15

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Observation eab4721d-2a01-4ab4-9915-e636f0e97292 · outbound

This paper cites Why do similarity matching objectives lead to hebbian/anti-hebbian networks?.

Neuroscience-inspired online unsupervised learning algorithms Why do similarity matching objectives lead to hebbian/anti-hebbian networks?

Reference 16

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Observation 69a85ca0-daf6-4c4f-b78c-bea8bb8bfc2b · outbound

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Neuroscience-inspired online unsupervised learning algorithms A hebbian/anti-hebbian neural network for linear subspace learning: A derivation from multidimensional scaling of streaming data,

Reference 17

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Observation 2c23685e-43b9-4243-bb05-0ff17fa1ee4a · outbound

This paper cites A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination.

Neuroscience-inspired online unsupervised learning algorithms A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination

Reference 18

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Observation f66f8fbb-966c-4d62-9451-6f3d5b1d50eb · outbound

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Neuroscience-inspired online unsupervised learning algorithms Optimization theory of hebbian/anti-hebbian networks for pca and whitening,

Reference 19

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This paper cites Biologically plausible online pca without recurrent dynamics,.

Neuroscience-inspired online unsupervised learning algorithms Biologically plausible online pca without recurrent dynamics,

Reference 20

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This paper cites A hebbian/anti-hebbian network derived from online non- negative matrix factorization can cluster and discover sparse features,.

Neuroscience-inspired online unsupervised learning algorithms A hebbian/anti-hebbian network derived from online non- negative matrix factorization can cluster and discover sparse features,

Reference 21

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This paper cites Online representation learning with single and multi-layer hebbian networks for image classification,.

Neuroscience-inspired online unsupervised learning algorithms Online representation learning with single and multi-layer hebbian networks for image classification,

Reference 22

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Neuroscience-inspired online unsupervised learning algorithms An analysis of single-layer networks in unsupervised feature learning,

Reference 23

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Neuroscience-inspired online unsupervised learning algorithms Convolutional deep belief networks on cifar-10,

Reference 24

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Neuroscience-inspired online unsupervised learning algorithms Blind nonnegative source separation using biological neural networks,

Reference 25

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Neuroscience-inspired online unsupervised learning algorithms A spiking neural network with local learning rules derived from nonnegative similarity matching,

Reference 26

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Neuroscience-inspired online unsupervised learning algorithms Building efficient deep hebbian networks for image classification tasks,

Reference 27

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Neuroscience-inspired online unsupervised learning algorithms The “independent components

Reference 28

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Neuroscience-inspired online unsupervised learning algorithms Conditions for nonnegative independent component analysis,

Reference 29

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Neuroscience-inspired online unsupervised learning algorithms Symmetric nonnegative matrix factorization for graph clustering,

Reference 30

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This paper cites Manifold-tiling localized receptive fields are optimal in similarity- preserving neural networks,.

Neuroscience-inspired online unsupervised learning algorithms Manifold-tiling localized receptive fields are optimal in similarity- preserving neural networks,

Reference 31

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Neuroscience-inspired online unsupervised learning algorithms Neural networks for efficient nonlinear online clustering,

Reference 32

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Neuroscience-inspired online unsupervised learning algorithms Random features for large-scale kernel machines,

Reference 33

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Neuroscience-inspired online unsupervised learning algorithms Berman and N

Reference 34

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Source-reported events for the cited work

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Observation c8e79bde-b827-4faa-bb91-2a5e33b9afc2 · outbound

This paper cites Robust and computationally feasible community detection in the presence of arbitrary outlier nodes,.

Neuroscience-inspired online unsupervised learning algorithms Robust and computationally feasible community detection in the presence of arbitrary outlier nodes,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 43f57823-4938-4fec-a347-cdfd456b595e · outbound

This paper cites A clustering neural network model of insect olfaction,.

Neuroscience-inspired online unsupervised learning algorithms A clustering neural network model of insect olfaction,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:08:07.385095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 302d1599-7b4d-4db4-b1b7-147e7c9dcbe6 · outbound

This paper cites Semi-supervised graph clustering: a kernel approach,.

Neuroscience-inspired online unsupervised learning algorithms Semi-supervised graph clustering: a kernel approach,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:08:07.367170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation cfb533c8-1cc6-42ce-8327-d52845099aa0 · outbound

This paper cites Clustering is semidefinitely not that hard: Nonnegative sdp for manifold disentangling,.

Neuroscience-inspired online unsupervised learning algorithms Clustering is semidefinitely not that hard: Nonnegative sdp for manifold disentangling,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:08:07.347957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 417469ee-0263-4046-a8f0-bac8ec6c95f7 · outbound

This paper cites On kernel-target alignment,.

Neuroscience-inspired online unsupervised learning algorithms On kernel-target alignment,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:08:07.329145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1739f09c-14b3-47cd-9719-03b413165aaf · outbound

This paper cites A hebbian/anti-hebbian network for online sparse dictionary learning derived from symmetric matrix factorization,.

Neuroscience-inspired online unsupervised learning algorithms A hebbian/anti-hebbian network for online sparse dictionary learning derived from symmetric matrix factorization,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T15:08:07.310763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T15:08:07.247629Z digest=sha256:4d9aadd5493cc03bc061e04721f5a86ee1600212f469dffb331c13467cd6fade

Pith citing papers

No inbound Pith citation observations are available.