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Paper Citation Record · LEDGER

Greedy dynamical meta-learning

As of 16 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 0 inbound Pith citation observations for arXiv:2607.23925.

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

pith.paper-citation-record.v1
2607.23925 v1

Coverage vector

measured 72 of 72 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:37:16.921049Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

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

Source: cited_works

Reference resolution

72 of 72 outbound references displayed

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External citation measurements

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Outbound references

Observation 566bce98-6326-455c-aac6-3b119a22f35a · outbound

This paper cites Learning to learn by gradient descent by gradient descent , year =.

Greedy dynamical meta-learning Learning to learn by gradient descent by gradient descent , year =

Reference 1

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Observation 2404a7d1-581e-439a-9b11-f405e0c494ba · outbound

This paper cites Selection and Reinforcement Learning for Combinatorial Optimization.

Greedy dynamical meta-learning Selection and Reinforcement Learning for Combinatorial Optimization

Reference 2

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This paper cites Language Models are Few-Shot Learners , url =.

Greedy dynamical meta-learning Language Models are Few-Shot Learners , url =

Reference 3

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This paper cites Proceedings of the 31st International Conference on Machine Learning , pages =.

Greedy dynamical meta-learning Proceedings of the 31st International Conference on Machine Learning , pages =

Reference 4

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Greedy dynamical meta-learning Unresolved cited work

Reference 5

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This paper cites Proceedings of The 33rd International Conference on Machine Learning , pages =.

Greedy dynamical meta-learning Proceedings of The 33rd International Conference on Machine Learning , pages =

Reference 6

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Greedy dynamical meta-learning 2016 , eprint=

Reference 7

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Greedy dynamical meta-learning Unresolved cited work

Reference 8

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Greedy dynamical meta-learning 1992 , institution =

Reference 9

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Greedy dynamical meta-learning 1990 , institution =

Reference 10

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Greedy dynamical meta-learning and Simard, P

Reference 11

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This paper cites Neural Processing Letters , volume =.

Greedy dynamical meta-learning Neural Processing Letters , volume =

Reference 12

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Greedy dynamical meta-learning Natural Computing , volume =

Reference 13

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Greedy dynamical meta-learning 1990 , institution =

Reference 14

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This paper cites Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies.

Greedy dynamical meta-learning Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies

Reference 15

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Greedy dynamical meta-learning , journal=

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Greedy dynamical meta-learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 17

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Greedy dynamical meta-learning Journal of Machine Learning Research , year =

Reference 18

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Greedy dynamical meta-learning Unresolved cited work

Reference 19

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Greedy dynamical meta-learning Unresolved cited work

Reference 20

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Greedy dynamical meta-learning Proceedings of the 34th International Conference on Machine Learning - Volume 70 , pages =

Reference 21

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Greedy dynamical meta-learning Mastering Atari with Discrete World Models

Reference 22

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Greedy dynamical meta-learning Deep Speech: Scaling up end-to-end speech recognition

Reference 23

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Greedy dynamical meta-learning Proceedings of the Sixth International Conference on Genetic Algorithms , pages =

Reference 24

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Greedy dynamical meta-learning and Ostermeier, A

Reference 25

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Greedy dynamical meta-learning Completely Derandomized Self-Adaptation in Evolution Strategies , year=

Reference 26

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Greedy dynamical meta-learning 1974 , issn =

Reference 27

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Greedy dynamical meta-learning 2016 , volume =

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Greedy dynamical meta-learning Deep Learning Scaling is Predictable, Empirically

Reference 29

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Greedy dynamical meta-learning Long Short-Term Memory , year =

Reference 30

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Greedy dynamical meta-learning 2022 , volume=

Reference 31

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Greedy dynamical meta-learning Universal Language Model Fine-tuning for Text Classification

Reference 32

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Greedy dynamical meta-learning Unresolved cited work

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Greedy dynamical meta-learning and Plaat, Aske , title =

Reference 34

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Greedy dynamical meta-learning 1997 , issn =

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Greedy dynamical meta-learning Scaling Laws for Neural Language Models

Reference 36

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Greedy dynamical meta-learning International Conference on Learning Representations , year=

Reference 37

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Greedy dynamical meta-learning 1978 , publisher=

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Greedy dynamical meta-learning A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Reference 39

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Greedy dynamical meta-learning Learning to Optimize

Reference 40

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Greedy dynamical meta-learning Learning to Optimize Neural Nets

Reference 41

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Greedy dynamical meta-learning Proceedings of the 36th International Conference on Machine Learning , pages =

Reference 42

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Observation b19f6565-10d4-4f4b-a498-232554239942 · outbound

This paper cites Regularizing and Optimizing LSTM Language Models.

Greedy dynamical meta-learning Regularizing and Optimizing LSTM Language Models

Reference 43

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Observation d3750d58-bbe2-4449-9c8e-1fe85792e41e · outbound

This paper cites Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 , pages =.

Greedy dynamical meta-learning Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 , pages =

Reference 44

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Observation ed1df084-d201-4f51-92c3-3c8b54129637 · outbound

This paper cites an unresolved cited work.

Greedy dynamical meta-learning Unresolved cited work

Reference 45

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Observation 3434f89c-5480-4c5a-9c20-2f122e006662 · outbound

This paper cites 1973 , publisher =.

Greedy dynamical meta-learning 1973 , publisher =

Reference 46

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source=arxiv_source observed=2026-07-31T23:37:13.993390Z digest=sha256:4700eb5f3b904727eff405c405e6aef82c51e53010c202294d153fb1bae78bf6

Observation 3f101f6f-40e9-4497-8bea-006e02b4b855 · outbound

This paper cites A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity.

Greedy dynamical meta-learning A Simple Modification in CMA-ES Achieving Linear Time and Space Complexity

Reference 47

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source=arxiv_source observed=2026-07-31T23:37:14.142210Z digest=sha256:77e0e7cdf56c907f025fd93164bfd7025be2d5d885433fe211918b3b34d14e1e

Observation 9d438e30-047d-4c10-8472-2cebe6861a40 · outbound

This paper cites and Jonsson, M.T.

Greedy dynamical meta-learning and Jonsson, M.T

Reference 48

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source=arxiv_source observed=2026-07-31T23:37:14.302538Z digest=sha256:07853f5f930e5f3562e526e5584c16d84f1bb3d1564d59255879a31222476f4d

Observation dc6c3869-eb87-46b9-9465-924bc9512342 · outbound

This paper cites Thirty-seventh Conference on Neural Information Processing Systems , year=.

Greedy dynamical meta-learning Thirty-seventh Conference on Neural Information Processing Systems , year=

Reference 49

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source=arxiv_source observed=2026-07-31T23:37:14.432025Z digest=sha256:6002fa15331c8fda701b8e28df34b003bc565feb8aa340f0fe09b822694ae8ea

Observation 52222929-2ea7-4c15-9108-d2f980a36222 · outbound

This paper cites Evolutionary Principles in Self-Referential Learning.

Greedy dynamical meta-learning Evolutionary Principles in Self-Referential Learning

Reference 50

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Observation b35063e7-7bd0-4c4d-ba60-a1b5ecbc9d34 · outbound

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Greedy dynamical meta-learning , booktitle=

Reference 51

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source=arxiv_source observed=2026-07-31T23:37:14.696801Z digest=sha256:fe74f88ef6011644de6f22f31091be0f3305dda43a9fe63a26df6a272584174c

Observation b05360cf-594f-4462-8e2e-66343efb4861 · outbound

This paper cites an unresolved cited work.

Greedy dynamical meta-learning Unresolved cited work

Reference 52

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Observation 4433881c-b74d-4fc4-8c58-1ba8464c96cc · outbound

This paper cites Mastering Atari, Go, chess and shogi by planning with a learned model , year=.

Greedy dynamical meta-learning Mastering Atari, Go, chess and shogi by planning with a learned model , year=

Reference 53

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source=arxiv_source observed=2026-07-31T23:37:14.890781Z digest=sha256:71f2f2c1dd6bbc22afe516d324f66381d40675ec87e3795fb6066e1ab7dc022c

Observation 899607bf-f639-4efb-a035-272d7ae7d4aa · outbound

This paper cites an unresolved cited work.

Greedy dynamical meta-learning Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-07-31T23:37:14.985687Z digest=sha256:c55c5efd6dd1f7bf602e5784157ee853ca07749fc29cbe05a342f59f577eda2c

Observation c3af7990-3cf9-446d-a735-9bda28fdb594 · outbound

This paper cites 2010 , note =.

Greedy dynamical meta-learning 2010 , note =

Reference 55

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source=arxiv_source observed=2026-07-31T23:37:15.058227Z digest=sha256:6d14e6680b0621004565fb8753c73dfc9b6d8efa0bea1269c41e8dbb01e8d558

Observation b2c2ac3f-ac1e-48d5-b304-131e82c9396c · outbound

This paper cites Mastering the game of Go without human knowledge , year=.

Greedy dynamical meta-learning Mastering the game of Go without human knowledge , year=

Reference 56

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source=arxiv_source observed=2026-07-31T23:37:15.151440Z digest=sha256:8ed28b8b73184ab5b637e4b304d6ceb1b38eb069b3acc770a440c94b2986e493

Observation d7088c2c-fca6-4996-9564-41c223d09ece · outbound

This paper cites , journal=.

Greedy dynamical meta-learning , journal=

Reference 57

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source=arxiv_source observed=2026-07-31T23:37:15.315131Z digest=sha256:95a30ac12833607b117394c8b11720aa0040bd92fbeb5458c416090a9b23567d

Observation 31a384ac-f276-41c0-9c23-20c54d363952 · outbound

This paper cites Highway Networks.

Greedy dynamical meta-learning Highway Networks

Reference 58

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source=arxiv_source observed=2026-07-31T23:37:15.363400Z digest=sha256:f86200f822249e55b97e04be4d9ae29f109b37aa61a7de27f4e5d179a70b566d

Observation 57d18538-06bf-4566-b319-7cf6616a3135 · outbound

This paper cites , title =.

Greedy dynamical meta-learning , title =

Reference 59

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source=arxiv_source observed=2026-07-31T23:37:15.464617Z digest=sha256:9d06e268c57b6688f0598f5ad8e4d204bb2a8dfc83138f2066891f08c3f57d89

Observation 4300e949-6f43-4b74-b765-b64ffef6c41f · outbound

This paper cites Learning to Learn: Introduction and Overview.

Greedy dynamical meta-learning Learning to Learn: Introduction and Overview

Reference 60

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verified exact
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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.

source=arxiv_source observed=2026-07-31T23:37:15.540856Z digest=sha256:3a5b31ae91811695e1a624de73640d85eee9b775b2beaf58bd5fe62fa36decbf

Observation 131f7bf6-2191-4918-9bae-3522dde86098 · outbound

This paper cites an unresolved cited work.

Greedy dynamical meta-learning Unresolved cited work

Reference 61

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verified exact
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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.

source=arxiv_source observed=2026-07-31T23:37:15.629983Z digest=sha256:1e0b7b5756fe987fb57f116ca811a89fc6ee102d01fa9161f783e6efd99bb67b

Observation 4698adc5-19db-43f9-901b-625aa29c00c6 · outbound

This paper cites Attention Is All You Need.

Greedy dynamical meta-learning Attention Is All You Need

Reference 62

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source=arxiv_source observed=2026-07-31T23:37:15.786868Z digest=sha256:c7eb2ac1cf84349862ab280e3f734883cf5bfa7abffc40a1a72db54e8f93bf55

Observation e6d69fb9-c6a3-46b7-bd0b-c792345f98a8 · outbound

This paper cites Residual Networks Behave Like Ensembles of Relatively Shallow Networks.

Greedy dynamical meta-learning Residual Networks Behave Like Ensembles of Relatively Shallow Networks

Reference 63

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no resolver link, observed 2026-07-31T23:37:15.914181Z

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source=arxiv_source observed=2026-07-31T23:37:15.914181Z digest=sha256:44537115f49dc28d4ac3cc244a59873b5775399bc3164832d247ceb219f744ad

Observation d838a9b1-b22a-4ce4-9529-6a107c3bd2cf · outbound

This paper cites Advances and Challenges in Meta-Learning: A Technical Review , year =.

Greedy dynamical meta-learning Advances and Challenges in Meta-Learning: A Technical Review , year =

Reference 64

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source=arxiv_source observed=2026-07-31T23:37:16.018034Z digest=sha256:54660ddc77b824c60cd7f5adec8d7984a2f1d59ac4b88d8de2e22ad1de8d9899

Observation 3bc74eed-0616-4e9e-9de5-34e86e1ae350 · outbound

This paper cites 2016 , eprint=.

Greedy dynamical meta-learning 2016 , eprint=

Reference 65

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source=arxiv_source observed=2026-07-31T23:37:16.114316Z digest=sha256:95aef2a836443ef4146b5e26e4d6fee1cc13bcc8429334f3dea23279127a5b5a

Observation 4be9f08d-f76c-49a4-9260-70b58e302a49 · outbound

This paper cites Transactions on Machine Learning Research , issn=.

Greedy dynamical meta-learning Transactions on Machine Learning Research , issn=

Reference 66

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source=arxiv_source observed=2026-07-31T23:37:16.245859Z digest=sha256:077597974fc7d2410ad096c41beca1fd2538c81faf769b1590f0375c5dbc3c31

Observation e9190747-f37d-46d4-9f00-9d191a6bbb49 · outbound

This paper cites Journal of Machine Learning Research , year =.

Greedy dynamical meta-learning Journal of Machine Learning Research , year =

Reference 67

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source=arxiv_source observed=2026-07-31T23:37:16.386546Z digest=sha256:400ac3f2dcfcbf52384aeebffe25c39cf45d7ee5c7d787636ded6a76d0141e3f

Observation 51bce009-77c0-4a5a-ab4f-35fc4e84070f · outbound

This paper cites Williams , title =.

Greedy dynamical meta-learning Williams , title =

Reference 68

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source=arxiv_source observed=2026-07-31T23:37:16.481831Z digest=sha256:2fb90baf8c8f3aef44f2bf854e6b5c861f391083c321500fd3a8a7c4c5c74acd

Observation 08f668f0-3cbc-4add-8479-58563843c7ff · outbound

This paper cites How transferable are features in deep neural networks?.

Greedy dynamical meta-learning How transferable are features in deep neural networks?

Reference 69

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source=arxiv_source observed=2026-07-31T23:37:16.581998Z digest=sha256:6306c012bde2c1e9f46abf9e9d1f0afaf9d1af04faa54d8c90f6042429c836a8

Observation 2e319e2c-9d7b-4b7a-ae83-9726b54d93be · outbound

This paper cites Recurrent Neural Network Regularization.

Greedy dynamical meta-learning Recurrent Neural Network Regularization

Reference 70

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source=arxiv_source observed=2026-07-31T23:37:16.693900Z digest=sha256:d6e8b1414304ec56f8f9917c20164eaea314f4849d010896fc0f4803d964056a

Observation 348de34a-c8aa-452d-aa90-ff31f8b1c4c0 · outbound

This paper cites 2021 , eprint=.

Greedy dynamical meta-learning 2021 , eprint=

Reference 71

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source=arxiv_source observed=2026-07-31T23:37:16.804072Z digest=sha256:73c48b839601a629de440593f7b873ff831d3331c13ec56506a0e24e3fdaf48a

Observation 6c271ea3-7049-40dd-891d-4c8f05b8a814 · outbound

This paper cites , journal=.

Greedy dynamical meta-learning , journal=

Reference 72

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source=arxiv_source observed=2026-07-31T23:37:16.921049Z digest=sha256:9c313e8c0f66009a8d8769b01023320eb29feb8a544e18877e40f11e0145addf

Pith citing papers

No inbound Pith citation observations are available.