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Paper Citation Record · LEDGER

Perspectives on Tsallis Statistics for Artificial Intelligence

As of 9 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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measured 0 of 0 inbound itemization

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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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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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Observation 09ff0ecb-6c0a-4303-8255-6dffac914d9b · outbound

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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Reference 19

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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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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

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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Perspectives on Tsallis Statistics for Artificial Intelligence Unresolved cited work

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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Observation ea36e03d-4eec-4a65-a8fc-d1c7ec0e5962 · outbound

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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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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.014747Z digest=sha256:0f68f5620f9b462c282b38502f82a510190a2797665606817ff868146fd69c3c

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=sha256:454a22b3c61ce793659bb9e3aa58e5b7257c2471d89a17198ab622510a1f5105

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.094746Z digest=sha256:6ac407938d8f8637f859aaac9aced4f205b060d053c5f3593a6b02671c08b03d

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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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:a401554a6d6116f1ec928e6dcc9d920e7743faddd17a58aedab76af4deb4e074

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.162553Z digest=sha256:479544a234d85535468686a6811bd8ec9255c19fa024af6af3da6a99fa79b4af

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.175368Z digest=sha256:55d6fdb990f7de5c2ebf8e7799b31e44aa01db74206572349c949092c4268eaf

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.186906Z digest=sha256:534375683372f5e630a5b17bb055fcab543013f4e7204421ca5ab3b8403c63c2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.215835Z digest=sha256:e88b2141774686301a518dfa4331b0aeb55cc32d4a49d3bbcdfdfff434bf9ef4

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.232705Z digest=sha256:b869f951140d5522a176fa6a2b965b25fc7f655c75bbc9c3ece6e0be84982097

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

This paper cites Tsallis, D.

Perspectives on Tsallis Statistics for Artificial Intelligence Tsallis, D

Reference 47

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.236249Z digest=sha256:86d6f0b40e11bb8fb540c4c9508a0c28f4a59ae71fe8d1685a612d1c1750e76a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.274744Z digest=sha256:aa33967a33a1f5a5b0e86b8f6e29a333c4a5c8d6caa304a2378a778b89d5ea8a

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.314842Z digest=sha256:e484592d78f1debbb9397629e095f0c840893a3a6253b7b4a95d0d3e7558b167

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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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.374752Z digest=sha256:9b9100e4c70d77f643460f8dded7b28bffc9418ee9f31b6428e1aac962ee3cb2

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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.414831Z digest=sha256:26994356dbb5e606610abfa055ff69f1b41bb83c7e91a15f0338edc886daff6e

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

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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=sha256:83c929feef4da09069536c8679db14596471149298d9df0afc06af676643ec76

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.484832Z digest=sha256:466908ff77dbeef27e52d9f6cfb5b45d01af50138dc4e62297dc88b9f6a671d7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.504752Z digest=sha256:e71fc2d037ca7c9ac1670dc645c74c8593601e6de184f1f3afaf2a0d2c241612

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.532149Z digest=sha256:3ea3cab63cebe2cc52f1452849eff3f71dbd57987878a76549be28118a33f4aa

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

This paper cites Amari, A.

Perspectives on Tsallis Statistics for Artificial Intelligence Amari, A

Reference 56

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:33:18.564756Z digest=sha256:b7cd0b384c491255baa4c2b8d2fc187582cf89bf8b833b695e259d198006608a

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

This paper cites Korbel, R.

Perspectives on Tsallis Statistics for Artificial Intelligence Korbel, R

Reference 57

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.583334Z digest=sha256:182cd05c2c21b32a6f6517d624ce366a0d89e2b30e6b95c2b1da4ad0f85ebd6b

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T00:33:18.591792Z digest=sha256:e6aa24f0837bb77085c234f98d5e02cbd82ba35bda719e6135949e5b0ab42439

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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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=sha256:2f4e8dfd83bf510896797c8e48f1b2282fd121574aaa6f8d303b163f29771f1e

Pith citing papers

No inbound Pith citation observations are available.