Paper Citation Record · LEDGER

Scaling of hardware-compatible perturbative training algorithms

As of 12 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2501.15403.

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

pith.paper-citation-record.v1
2501.15403 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:26:25.962597Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 788d188f-1db3-4ca6-8fc8-970f4989ff9a · outbound

This paper cites & Fitch, A.

Scaling of hardware-compatible perturbative training algorithms & Fitch, A

Reference 1

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Observation d010a68c-6f01-4516-a8fa-c9d13e55e046 · outbound

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

Scaling of hardware-compatible perturbative training algorithms The Forward-Forward Algorithm: Some Preliminary Investigations

Reference 2

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Observation 1f7c5ba6-36cf-4597-a217-9ea8a92f7e80 · outbound

This paper cites P., Santoro, A., Marris, L., Akerman, C.

Scaling of hardware-compatible perturbative training algorithms P., Santoro, A., Marris, L., Akerman, C

Reference 3

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 4

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Observation 870d5b29-7bb5-4830-9a6d-6a3ad515bb24 · outbound

This paper cites Science 380, 398–404.

Scaling of hardware-compatible perturbative training algorithms Science 380, 398–404

Reference 5

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This paper cites an unresolved cited work.

Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 6

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Observation a9f8fb05-1e80-472f-98f8-35decbf9b21a · outbound

This paper cites an unresolved cited work.

Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 7

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Observation 76821d90-ba6a-481f-b395-96c1dd2dce80 · outbound

This paper cites Nature Communications 2023 14:1 14, 1–18.

Scaling of hardware-compatible perturbative training algorithms Nature Communications 2023 14:1 14, 1–18

Reference 8

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Observation f3d88631-074b-4064-9c35-15ba6415be44 · outbound

This paper cites & McCaughan, A.

Scaling of hardware-compatible perturbative training algorithms & McCaughan, A

Reference 9

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Observation 7260fc34-2651-4b23-80bc-d974290bafb6 · outbound

This paper cites https://www.science.org/doi/10.1126/sciadv.ado8999 (28 July 2024).

Scaling of hardware-compatible perturbative training algorithms https://www.science.org/doi/10.1126/sciadv.ado8999 (28 July 2024)

Reference 10

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 11

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Observation 89f42011-68c4-48e0-b4ee-f98aa6b08b66 · outbound

This paper cites an unresolved cited work.

Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 12

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Observation 2396ed02-007a-4e92-8448-86ca05709062 · outbound

This paper cites an unresolved cited work.

Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 13

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Observation 0291fed0-45ee-4476-ae18-1c95f9ea2892 · outbound

This paper cites Frontiers in Neural Circuits 0, 53.

Scaling of hardware-compatible perturbative training algorithms Frontiers in Neural Circuits 0, 53

Reference 14

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Observation 703e0733-6dbf-416d-b439-1c664a4e7b69 · outbound

This paper cites & Kailath, T.

Scaling of hardware-compatible perturbative training algorithms & Kailath, T

Reference 15

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Observation a5b44ae2-306d-48f6-8caa-10508e901c22 · outbound

This paper cites & Koga, M.

Scaling of hardware-compatible perturbative training algorithms & Koga, M

Reference 16

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Observation 31a38e50-6c31-4fb3-b71b-b6c4c9dfac40 · outbound

This paper cites A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization in Advances in Neural Information Processing Systems 5 (NIPS 1992) (1992), 244–251.

Scaling of hardware-compatible perturbative training algorithms A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization in Advances in Neural Information Processing Systems 5 (NIPS 1992) (1992), 244–251

Reference 17

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Observation f50d2042-d700-479b-b746-03755a43706e · outbound

This paper cites & Jabri, M.

Scaling of hardware-compatible perturbative training algorithms & Jabri, M

Reference 18

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Observation 14c2d703-6469-4a4e-9b6f-f30276686707 · outbound

This paper cites Alspector, R.

Scaling of hardware-compatible perturbative training algorithms Alspector, R

Reference 19

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Observation 76228c3b-6898-4a99-8c73-8e7233c65ec7 · outbound

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 20

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Observation 4e9ceff3-839b-44d2-aa6c-7af36f1ff8f9 · outbound

This paper cites & Kanata, Y.

Scaling of hardware-compatible perturbative training algorithms & Kanata, Y

Reference 21

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Observation 042b7664-594b-40f4-b9d5-36e7d6f99e1a · outbound

This paper cites An analog VLSI recurrent neural network learning a continuous-time trajectory.

Scaling of hardware-compatible perturbative training algorithms An analog VLSI recurrent neural network learning a continuous-time trajectory

Reference 22

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Observation 96e736e3-0e2f-481a-aef8-b22d5dbe6c50 · outbound

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Scaling of hardware-compatible perturbative training algorithms & Fiesler, E

Reference 23

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Observation 17265be3-78ec-4f75-a8df-31813b206ac3 · outbound

This paper cites J., Gyurcsik, R.

Scaling of hardware-compatible perturbative training algorithms J., Gyurcsik, R

Reference 24

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Observation b276c8c9-71d7-427f-b81f-4ca22c5705d7 · outbound

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Scaling of hardware-compatible perturbative training algorithms & Matsumoto, T

Reference 25

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Observation 26f91214-3488-4a09-b138-6e5fff5ee1e8 · outbound

This paper cites Neural Networks in Analog Hardware - Design and Implementation Issues.

Scaling of hardware-compatible perturbative training algorithms Neural Networks in Analog Hardware - Design and Implementation Issues

Reference 26

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Observation 3957f906-7d82-47ef-8adb-ad17072c507a · outbound

This paper cites Analog VLSI Autonomous Systems for Learning and Optimization PhD thesis (Caltech, 1994).

Scaling of hardware-compatible perturbative training algorithms Analog VLSI Autonomous Systems for Learning and Optimization PhD thesis (Caltech, 1994)

Reference 27

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Observation 6a4782a1-d926-497d-b05f-16b7153649aa · outbound

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 28

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Observation 8d100268-1d1f-4f73-a6ac-9ce38e4af91a · outbound

This paper cites Node Perturbation Can Effectively Train Multi-Layer Neural Networks.

Scaling of hardware-compatible perturbative training algorithms Node Perturbation Can Effectively Train Multi-Layer Neural Networks

Reference 29

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Observation 6ee81e4e-4fd0-43df-b402-61312782fb8d · outbound

This paper cites Scaling Forward Gradient With Local Losses.

Scaling of hardware-compatible perturbative training algorithms Scaling Forward Gradient With Local Losses

Reference 30

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Observation ffe2364f-0059-4222-98b6-3b8fd3eb18ff · outbound

This paper cites Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks.

Scaling of hardware-compatible perturbative training algorithms Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks

Reference 31

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Observation 1c985b14-056e-4358-969b-f321ea7a87b6 · outbound

This paper cites Single chip photonic deep neural network with accelerated training.

Scaling of hardware-compatible perturbative training algorithms Single chip photonic deep neural network with accelerated training

Reference 32

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 33

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Observation 440c4199-daf8-451e-91ad-09d805931ce6 · outbound

This paper cites P., Cownden, D., Tweed, D.

Scaling of hardware-compatible perturbative training algorithms P., Cownden, D., Tweed, D

Reference 34

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Observation e5adbaea-ca13-4b52-8c58-63ab3ed73454 · outbound

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 35

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Observation 8a5fada7-39ce-404f-b59b-7278e3610a31 · outbound

This paper cites & Memmesheimer, R.

Scaling of hardware-compatible perturbative training algorithms & Memmesheimer, R

Reference 36

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Observation 4ad7988e-fafc-4ffe-ae54-1280fd154243 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Scaling of hardware-compatible perturbative training algorithms Adam: A Method for Stochastic Optimization

Reference 37

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Scaling of hardware-compatible perturbative training algorithms & Hinton, G

Reference 38

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 39

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Observation 211ce802-bf91-46b2-81b0-1ed7a8ffd687 · outbound

This paper cites B., Zhu, Q.

Scaling of hardware-compatible perturbative training algorithms B., Zhu, Q

Reference 40

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This paper cites How we created neuromorphic engineering.

Scaling of hardware-compatible perturbative training algorithms How we created neuromorphic engineering

Reference 41

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Observation 89a1a2e1-e990-4eb8-a549-76fafdf1fceb · outbound

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Scaling of hardware-compatible perturbative training algorithms P., Kim, H., Budhathoki, R

Reference 42

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Scaling of hardware-compatible perturbative training algorithms & Yao, W

Reference 43

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

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Observation fc7c5287-c3b7-4e19-8501-7bedb10837d2 · outbound

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 45

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Observation 8c4a306c-5299-4e25-8245-396862b89905 · outbound

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Scaling of hardware-compatible perturbative training algorithms & Seung, H

Reference 46

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 47

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Observation 2ba89773-aaff-4db9-98eb-0bb241d464ba · outbound

This paper cites Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks.

Scaling of hardware-compatible perturbative training algorithms Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks

Reference 48

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Observation 76ed7de7-314d-4c3d-8b8e-f4afa5c30b95 · outbound

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Scaling of hardware-compatible perturbative training algorithms Unresolved cited work

Reference 49

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Observation 70e296b1-838a-4e7d-a420-a109fb1b3255 · outbound

This paper cites Fine-Tuning Language Models with Just Forward Passes.

Scaling of hardware-compatible perturbative training algorithms Fine-Tuning Language Models with Just Forward Passes

Reference 50

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Observation 68b40dc8-cfa9-44f4-82a0-b14ea6a8f9a3 · outbound

This paper cites DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training.

Scaling of hardware-compatible perturbative training algorithms DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

Reference 51

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Observation 812a9399-e183-469c-a2d7-fef86edc55de · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Scaling of hardware-compatible perturbative training algorithms Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 52

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Pith citing papers

No inbound Pith citation observations are available.