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Paper Citation Record · LEDGER

Perspectives on Tsallis Statistics for Artificial Intelligence

As of 7 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2608.01223.

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

pith.paper-citation-record.v1
2608.01223 v1

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measured 59 of 59 reference resolution

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Reference resolution

59 of 59 outbound references displayed

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Outbound references

Observation db8c61e3-3734-4bfb-beaa-9bf2415b993a · outbound

This paper cites Tsallis, Possible generalization of Boltzmann–Gibbs statistics, Journal of Statistical Physics 52 (1988) 479–487.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Possible generalization of Boltzmann–Gibbs statistics, Journal of Statistical Physics 52 (1988) 479–487

Reference 1

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 2

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Observation a72cf68e-e736-485c-9344-e2c5871e55ce · outbound

This paper cites Entropy measures and their applications: A comprehensive review.

Perspectives on Tsallis Statistics for Artificial Intelligence Entropy measures and their applications: A comprehensive review

Reference 3

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Observation 84b2e5bd-6ae0-4750-8e20-f297742fa126 · outbound

This paper cites Tsallis, Introduction to Nonextensive Statistical Mechanics: Ap- proaching a Complex World, Springer, New York, 2009.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Introduction to Nonextensive Statistical Mechanics: Ap- proaching a Complex World, Springer, New York, 2009

Reference 4

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Observation feba4b6c-a055-49ce-996c-565f5ae714c4 · outbound

This paper cites Naudts, Generalised Thermostatistics, Springer, London, 2011.

Perspectives on Tsallis Statistics for Artificial Intelligence Naudts, Generalised Thermostatistics, Springer, London, 2011

Reference 5

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 6

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Observation cb69caf9-5457-4cb3-9dff-66227b537d51 · outbound

This paper cites Gell-Mann, C.

Perspectives on Tsallis Statistics for Artificial Intelligence Gell-Mann, C

Reference 7

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Observation c9d34bac-99fb-4992-961d-d05abe30a15a · outbound

This paper cites Tsallis, Beyond Boltzmann–Gibbs–Shannon in physics and elsewhere, Entropy 21 (2019) 696.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, Beyond Boltzmann–Gibbs–Shannon in physics and elsewhere, Entropy 21 (2019) 696

Reference 8

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This paper cites Penrose, Foundations of Statistical Mechanics: A Deductive Treat- ment, Pergamon Press, Oxford, 1970.

Perspectives on Tsallis Statistics for Artificial Intelligence Penrose, Foundations of Statistical Mechanics: A Deductive Treat- ment, Pergamon Press, Oxford, 1970

Reference 9

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Observation 560e194e-af02-4f9f-8fc8-f5fbb9b2e459 · outbound

This paper cites Furuichi, Information theoretical properties of Tsallis entropies, Jour- nal of Mathematical Physics 47 (2006) 023302.

Perspectives on Tsallis Statistics for Artificial Intelligence Furuichi, Information theoretical properties of Tsallis entropies, Jour- nal of Mathematical Physics 47 (2006) 023302

Reference 10

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Observation 0b321bc6-2ecd-4ecf-a664-62798bf225bb · outbound

This paper cites Tsallis, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, R

Reference 11

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Observation a69c35da-6166-4468-8aab-932a0fed45dd · outbound

This paper cites Nielsen, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Nielsen, R

Reference 12

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This paper cites Hanel, S.

Perspectives on Tsallis Statistics for Artificial Intelligence Hanel, S

Reference 13

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 14

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This paper cites Umarov, C.

Perspectives on Tsallis Statistics for Artificial Intelligence Umarov, C

Reference 15

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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 16

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This paper cites Prato, C.

Perspectives on Tsallis Statistics for Artificial Intelligence Prato, C

Reference 17

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Perspectives on Tsallis Statistics for Artificial Intelligence Peters, V

Reference 20

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Perspectives on Tsallis Statistics for Artificial Intelligence Blondel, A

Reference 22

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse and Continuous Attention Mechanisms

Reference 23

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse Continuous Distributions and Fenchel-Young Losses

Reference 24

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Perspectives on Tsallis Statistics for Artificial Intelligence Gonçalves, M

Reference 25

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Perspectives on Tsallis Statistics for Artificial Intelligence Vasylenko, H

Reference 26

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Perspectives on Tsallis Statistics for Artificial Intelligence Haarnoja, A

Reference 27

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Reference 28

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Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning

Reference 29

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Reference 30

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Perspectives on Tsallis Statistics for Artificial Intelligence A Theory of Regularized Markov Decision Processes

Reference 31

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Perspectives on Tsallis Statistics for Artificial Intelligence Munchausen Reinforcement Learning

Reference 32

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Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits

Reference 33

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Perspectives on Tsallis Statistics for Artificial Intelligence Zhang, S

Reference 34

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Observation 6db4eafb-e19a-4c80-b130-8b1c4c7adb7e · outbound

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Perspectives on Tsallis Statistics for Artificial Intelligence Predicting Attention Sparsity in Transformers

Reference 35

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Perspectives on Tsallis Statistics for Artificial Intelligence Sparse Graph Attention Networks

Reference 36

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Observation b5f568c6-942a-46b7-b9a3-389a18881094 · outbound

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Perspectives on Tsallis Statistics for Artificial Intelligence Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Reference 37

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Observation 693170bc-9347-44aa-be91-9e58663ce955 · outbound

This paper cites an unresolved cited work.

Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 38

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raw_fallback, observed 2026-08-06T00:33:25.065521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.014747Z digest=sha256:93c63f6cda4c2fb9171d2f8284065499a760234b8cc8f45000ecb38ac4d19fa3

Observation ba1efa52-8213-4440-85cf-2de89efdb523 · outbound

This paper cites Takahashi, T.

Perspectives on Tsallis Statistics for Artificial Intelligence Takahashi, T

Reference 39

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malformed identifier
no resolver link, observed 2026-08-06T00:33:18.055086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.055086Z digest=sha256:6d9c69925e561eba8505acbc6fcebbd9991168a569a336d0289403329e4366dc

Observation e1fe679a-dd15-455d-a896-2536951e468c · outbound

This paper cites Star-Shaped Denoising Diffusion Probabilistic Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Star-Shaped Denoising Diffusion Probabilistic Models

Reference 40

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local_arxiv, observed 2026-08-06T00:33:20.365698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.094746Z digest=sha256:3d1a57b8ce6794f5391fb2f7f493364d94c2126feec93c40b07610bfbd3dad69

Observation e78e911a-1f8d-40eb-b81c-359ba12563a7 · outbound

This paper cites Heavy-Tailed Diffusion Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy-Tailed Diffusion Models

Reference 41

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unresolved
no resolver link, observed 2026-08-06T00:33:18.144751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.144751Z digest=sha256:f72226b90b49fa687e63a07404b14923b3ee82b4d961e1d885248ddd6ce1e2a7

Observation a30a92e3-40fe-461c-b85d-e0a4d579f3a2 · outbound

This paper cites Heavy-Tailed Diffusion with Denoising L\'evy Probabilistic Models.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy-Tailed Diffusion with Denoising L\'evy Probabilistic Models

Reference 42

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.324152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.162553Z digest=sha256:5f9dd1e62f355938443948dcefeac7ecc817b6d6b7b93c9c0cfd7ef7d7d040c3

Observation 7be93606-f979-4bd9-8d49-ed6d652fa2aa · outbound

This paper cites van der Maaten, G.

Perspectives on Tsallis Statistics for Artificial Intelligence van der Maaten, G

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:24.790634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.175368Z digest=sha256:110f97afba3c28439329a9382ac1f00134708a59e889175d051af2fa50f6109b

Observation 2df78168-a4ff-4125-8f72-dd6bde5c25fb · outbound

This paper cites Zhang, M.

Perspectives on Tsallis Statistics for Artificial Intelligence Zhang, M

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:24.484756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.186906Z digest=sha256:8480ea2a169f87cfe28ae11829539c3480aeda1e3b0010ecf768948a852bc5b4

Observation 9c2ca8b8-6d57-477a-8efd-a9b7b3bf4ebb · outbound

This paper cites Two-temperature logistic regression based on the Tsallis divergence.

Perspectives on Tsallis Statistics for Artificial Intelligence Two-temperature logistic regression based on the Tsallis divergence

Reference 45

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.288694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.215835Z digest=sha256:a9eb52786862b972fd40771a13bbbfa08273f3fa5a4e59b20f7fc66d2a613f08

Observation 70d28c16-3929-472e-8fff-2a26cbf897df · outbound

This paper cites Robust Bi-Tempered Logistic Loss Based on Bregman Divergences.

Perspectives on Tsallis Statistics for Artificial Intelligence Robust Bi-Tempered Logistic Loss Based on Bregman Divergences

Reference 46

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.228332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.232705Z digest=sha256:0e72b45193152031a77bda31fbe06ace5491b726135d8e8cd964c212d4efe396

Observation e886a9b3-632a-40f7-be40-edfdc96a5112 · outbound

This paper cites Tsallis, D.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, D

Reference 47

Resolution
verified exact
doi, observed 2026-08-06T00:33:19.054763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.236249Z digest=sha256:935f701f30bb939b4f153bdab2964b49e4e18ae2cb8cf2903966c5e576e63b27

Observation 1731f578-7e0b-4e2a-828e-1ffb90bb9e5f · outbound

This paper cites The q-gradient method for global optimization.

Perspectives on Tsallis Statistics for Artificial Intelligence The q-gradient method for global optimization

Reference 48

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.114750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.274744Z digest=sha256:02f96d0095ccb8177d8be7fbdd356364278318da91c9205e0ae6b744ec05717f

Observation f5ad3b19-7341-4f56-a864-f26b9dc1b0c6 · outbound

This paper cites an unresolved cited work.

Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-06T00:33:24.164754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.314842Z digest=sha256:b67e2b4ac97a882ecff7da0a181370c01db36dfdec5eb55f90ee1ef4d5204930

Observation cff575e7-54cb-4149-93f8-ebb94254f8f1 · outbound

This paper cites Şimşekli, L.

Perspectives on Tsallis Statistics for Artificial Intelligence Şimşekli, L

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-06T00:33:23.804032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.374752Z digest=sha256:7b9f630ef98df949b19899920199682f9a28ce8a4a7615a7d06b4c00b1c28b31

Observation ec3d0365-8012-44ca-a753-86b354271d46 · outbound

This paper cites The Heavy-Tail Phenomenon in SGD.

Perspectives on Tsallis Statistics for Artificial Intelligence The Heavy-Tail Phenomenon in SGD

Reference 51

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verified exact
local_arxiv, observed 2026-08-06T00:33:20.044752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.414831Z digest=sha256:6ec1073e1afedcc1a09b428eef5b3f53a0ddad57d6f3fa32fdc11bc1403e6636

Observation 2df48e7d-70d9-4c6f-aca7-32b3fc4a2ae0 · outbound

This paper cites Multiplicative noise and heavy tails in stochastic optimization.

Perspectives on Tsallis Statistics for Artificial Intelligence Multiplicative noise and heavy tails in stochastic optimization

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T00:33:18.454829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.454829Z digest=sha256:2ef5599e65eea85ca6baaee05ded83e279fcfdb6c053439e6cdbd2c962922ef6

Observation 73a61d7f-1348-4bff-8101-0779609e941c · outbound

This paper cites Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks.

Perspectives on Tsallis Statistics for Artificial Intelligence Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-06T00:33:19.871401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.484832Z digest=sha256:58acf0cf1314a894aaf233fd3d523af8c3227da5dbad5cfcb7f4cbd83c4bae7b

Observation baea902a-07eb-4d84-bddc-4185221bfbc5 · outbound

This paper cites Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks.

Perspectives on Tsallis Statistics for Artificial Intelligence Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks

Reference 54

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verified exact
local_arxiv, observed 2026-08-06T00:33:19.808401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.504752Z digest=sha256:ea3c6132e2768c24ef262264db2f273d0b41cd6c7f12589cacd248fb7e4762b6

Observation d4855ecc-eccf-4605-bce6-6e989a0a2545 · outbound

This paper cites Amari, Information Geometry and Its Applications, volume 194 ofApplied Mathematical Sciences, Springer, 2016.

Perspectives on Tsallis Statistics for Artificial Intelligence Amari, Information Geometry and Its Applications, volume 194 ofApplied Mathematical Sciences, Springer, 2016

Reference 55

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no resolver link, observed 2026-08-06T00:33:18.532149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.532149Z digest=sha256:46526a420541738efc5eb217824f8de9aaf171ef3f1a778fd5a8fecc6e83b5bd

Observation 557e15ca-7506-4dec-b2c3-96f1b3237c6a · outbound

This paper cites Amari, A.

Perspectives on Tsallis Statistics for Artificial Intelligence Amari, A

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T00:33:18.564756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.564756Z digest=sha256:6f8c5f4122e96c1463dd691ef1042d2f9b4141c715ca4a43c9936bfa78face7a

Observation 34f1bc68-22d0-4da0-9a98-06342ba57cf3 · outbound

This paper cites Korbel, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Korbel, R

Reference 57

Resolution
verified exact
doi, observed 2026-08-06T00:33:18.724905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.583334Z digest=sha256:a30cdfc687ac0194fb2a4bdde081ba698d467f116a90355ae2928eafb01994e0

Observation 2ce07f68-61b3-48da-a2d0-514596e74372 · outbound

This paper cites w_key"]) @ params[.

Perspectives on Tsallis Statistics for Artificial Intelligence w_key"]) @ params[

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T00:33:23.474271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T00:33:18.591792Z digest=sha256:ac77b6b2f0508b965f5c90b84a2b84d513d9112b5afa4bf2f578213993975433

Observation 2cb0968c-b162-4856-bb1f-8958827466ae · outbound

This paper cites Why are Adaptive Methods Good for Attention Models?.

Perspectives on Tsallis Statistics for Artificial Intelligence Why are Adaptive Methods Good for Attention Models?

Reference 2020

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unresolved
no resolver link, observed 2026-08-06T00:33:17.824749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:17.824749Z digest=sha256:57815a17631b24e6b8c12cfe29e523b6550fe29ce5e9dd4d0bf78a00863ff05b

Pith citing papers

No inbound Pith citation observations are available.