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Paper Citation Record · LEDGER

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning

As of 22 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2501.09611.

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

pith.paper-citation-record.v1
2501.09611 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:55:36.053274Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

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

45 of 45 outbound references displayed

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  • verified fuzzy35
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c9d7ef1-1277-49e7-b42a-5512854fa1f4 · outbound

This paper cites Deep Reinforcement Learning at the Edge of the Statistical Precipice.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Deep Reinforcement Learning at the Edge of the Statistical Precipice

Reference 1

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Source-reported events for the cited work

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Observation b6754f36-cb55-4b71-a168-0b2094679865 · outbound

This paper cites Optimistic Posterior Sampling for Reinforcement Learning: Worst-Case Regret Bounds.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Optimistic Posterior Sampling for Reinforcement Learning: Worst-Case Regret Bounds

Reference 2

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source=pdf_text observed=2026-08-10T19:55:35.889367Z digest=sha256:d0ba9604757b1c008a6b966dd4710e38f84bd1cb17c0fa8c506617d940c321c9

Observation 2d3b2cae-5733-4431-8455-f41cfa77d442 · outbound

This paper cites State-Aware Variational Thompson Sampling for Deep Q-Networks.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning State-Aware Variational Thompson Sampling for Deep Q-Networks

Reference 3

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source=pdf_text observed=2026-08-10T19:55:35.893260Z digest=sha256:f52498dcf5f25f2f2a56d120eebc147ad9d25fdf13366797be9e5390ea4b9ddf

Observation b62440ba-6fa3-4898-9788-5e8dc3b96899 · outbound

This paper cites Efficient Exploration through Bayesian Deep Q- Networks.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Efficient Exploration through Bayesian Deep Q- Networks

Reference 4

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source=pdf_text observed=2026-08-10T19:55:35.897101Z digest=sha256:b8596a2dd603167bb0e561827620e7583cd1c79ffc80bc975a2657872e0d65de

Observation 9c906764-9496-4073-86e1-c5c11d07e841 · outbound

This paper cites Campbell, and Sergey Levine.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Campbell, and Sergey Levine

Reference 5

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source=pdf_text observed=2026-08-10T19:55:35.901257Z digest=sha256:2f9ce2134f8d8c4cf7fb09682945d9a6fb69a37768b16768c020551464bfbaec

Observation 47e0cfef-0713-421d-abc6-3240d1cabc59 · outbound

This paper cites Unifying Count-Based Explo- ration and Intrinsic Motivation.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Unifying Count-Based Explo- ration and Intrinsic Motivation

Reference 6

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source=pdf_text observed=2026-08-10T19:55:35.904886Z digest=sha256:f20835d6ec980c27e0f3bfbe54ce41009ee6bb6ef175f8358ee4aba0c7f81829

Observation 3911ed0d-f654-4491-839e-069a8661d874 · outbound

This paper cites Path Integral Guided Policy Search.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Path Integral Guided Policy Search

Reference 8

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Observation 94a89d7f-b0ad-46b5-aab9-716f0b2e134d · outbound

This paper cites Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search

Reference 9

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source=pdf_text observed=2026-08-10T19:55:35.920060Z digest=sha256:e7376822bfaa6386bdd77a3cb42c1d45a4005d0dc0aadfd2ebf2aff3db81f8b5

Observation 4ff8066b-a52f-464b-8ba5-f5194da92c20 · outbound

This paper cites Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Reference 10

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source=pdf_text observed=2026-08-10T19:55:35.923681Z digest=sha256:102db47b29c4f887c6c34eb6945e61caddc0c56d200ba4561e07493287069f6e

Observation 79540c1f-c4d6-434f-b876-7135487f73b9 · outbound

This paper cites Noisy Networks for Exploration.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Noisy Networks for Exploration

Reference 11

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source=pdf_text observed=2026-08-10T19:55:35.927187Z digest=sha256:2363d5b39086bd37fde04ebda14629ce67b79c5bf2df3d21f67a6bc6c51ee781

Observation 2afb9886-7567-4765-b58a-4339e86e892d · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 12

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source=pdf_text observed=2026-08-10T19:55:35.931391Z digest=sha256:599f9c9b803f197530c1100192757dd00f88a1a797148a73be92fe3b95597336

Observation a7ed6cfa-5fef-422f-9d3d-b0cdc602125c · outbound

This paper cites T emporal Difference Variational Auto-Encoder.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning T emporal Difference Variational Auto-Encoder

Reference 13

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source=pdf_text observed=2026-08-10T19:55:35.935132Z digest=sha256:2bc655c0cddab9b7831950a243ace0ef2544584b85f59713fa5001f57d644a3b

Observation 1ba54c0f-8fb2-434a-812a-65472681390f · outbound

This paper cites Recurrent World Models Facilitate Policy Evolution.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Recurrent World Models Facilitate Policy Evolution

Reference 14

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source=pdf_text observed=2026-08-10T19:55:35.938524Z digest=sha256:18b475b608af5387553c863ae72678ecfb1eeae6373ba56c2b5fcd4d77f8a3b7

Observation 0c75bf1e-9bce-42f8-879f-7625e0e10b29 · outbound

This paper cites Learning Latent Dynamics for Planning from Pixels.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Learning Latent Dynamics for Planning from Pixels

Reference 15

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source=pdf_text observed=2026-08-10T19:55:35.941898Z digest=sha256:e5ba3eed50995f8423df3ad06933d3972dd8482af809b855c5c02ebe021136d6

Observation 9039ec38-4961-4629-9880-ddc0abe72d25 · outbound

This paper cites Harris, K.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Harris, K

Reference 16

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source=pdf_text observed=2026-08-10T19:55:35.945334Z digest=sha256:eb6757a4d5074f4be10edb81554d6f223ae00468fd473139ee345322df84cb8d

Observation c1817ba5-d31d-4e8b-9f5e-7fcf1a75eeac · outbound

This paper cites Deep reinforcement learning that matters.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Deep reinforcement learning that matters

Reference 17

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source=pdf_text observed=2026-08-10T19:55:35.948800Z digest=sha256:3d00e4cbb91b4efbbdbd4911bd0de7e7ef8fc7fe78b39e8d6984da0a2ca0b2be

Observation c3f814c6-7c94-4af6-9b1f-049a7a79a48b · outbound

This paper cites Near-Optimal Regret Bounds for Reinforcement Learning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Near-Optimal Regret Bounds for Reinforcement Learning

Reference 18

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source=pdf_text observed=2026-08-10T19:55:35.952307Z digest=sha256:af4222a0276c67fc92d986838da7ee2c37f1a23f0dd6b77c8d91f7f44cd24b76

Observation 59abbed8-df8b-4879-82e4-c999e0097802 · outbound

This paper cites Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Importance of using appropriate baselines for evaluation of data-efficiency in deep reinforcement learning for Atari

Reference 19

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source=pdf_text observed=2026-08-10T19:55:35.955722Z digest=sha256:88aec9eec072bd09217cca22f7a3367e7ac1bd9db4b4ec578a2465e30c8cc4ff

Observation 91ec0fbb-8a00-467d-ab43-64022287304d · outbound

This paper cites V ariational Dropout and the Local Reparameterization Trick.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning V ariational Dropout and the Local Reparameterization Trick

Reference 20

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source=pdf_text observed=2026-08-10T19:55:35.959767Z digest=sha256:5e22e3a45b08b918d1d69cc8ad07df341214c18969b0a30052019556b233cd62

Observation c4ddc1dc-9f5b-4b34-a035-9d63e5d7c0ab · outbound

This paper cites CURL: Contrastive Unsupervised Representations for Reinforcement Learn- ing.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning CURL: Contrastive Unsupervised Representations for Reinforcement Learn- ing

Reference 21

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source=pdf_text observed=2026-08-10T19:55:35.963307Z digest=sha256:c20dffc113ec9c5ee8e7580a2a533277ee781e0de003a6042814452b09cd27c8

Observation 5136596c-1042-4109-9d92-e51fda210235 · outbound

This paper cites Guided Policy Search.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Guided Policy Search

Reference 22

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source=pdf_text observed=2026-08-10T19:55:35.967148Z digest=sha256:39c982563e0778afcbddc3c6cfb3bf603bf25c942f72015e3691cb117dc1e5b6

Observation 74e99bcc-5da9-4560-9591-fb50c9421966 · outbound

This paper cites Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Reference 23

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source=pdf_text observed=2026-08-10T19:55:35.970683Z digest=sha256:4bb7bd4bdb046cfa2e8441820b83e08f14f7264f6d8a1d8dd2695f1f74d900f2

Observation 86bcb8c5-7652-4c65-8e2d-606352d0bbe6 · outbound

This paper cites Action-Conditional Video Prediction Using Deep Networks in Atari Games.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Action-Conditional Video Prediction Using Deep Networks in Atari Games

Reference 24

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source=pdf_text observed=2026-08-10T19:55:35.974509Z digest=sha256:46503d641efabc585bf1f78fd4c62a4760e5020b80a4e0f0dcbb84a42925d011

Observation 579b6bf4-56eb-4925-a5a5-02fb1a54a727 · outbound

This paper cites Bootstrapped Thompson Sampling and Deep Exploration.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Bootstrapped Thompson Sampling and Deep Exploration

Reference 25

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source=pdf_text observed=2026-08-10T19:55:35.977781Z digest=sha256:a6721e8e4a0f413d1d5e5bc973966543258b1995e77cf557af2465473ef7ef33

Observation 31acf276-8501-47d5-9c22-f2131e982511 · outbound

This paper cites Why is Posterior Sampling Better than Optimism for Reinforcement Learning? InInternational Conference on Machine Learning , pages 2701–2710, 2017.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Why is Posterior Sampling Better than Optimism for Reinforcement Learning? InInternational Conference on Machine Learning , pages 2701–2710, 2017

Reference 26

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source=pdf_text observed=2026-08-10T19:55:35.981516Z digest=sha256:600fa4fc5b3e3790986b4b93e4cb4a39c859aadc0dc27ad1f0a528adbe1d5a0e

Observation eb08a996-4601-4d49-8cee-e48b2dbc7df9 · outbound

This paper cites (More) Efficient Reinforcement Learning via Posterior Sampling.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning (More) Efficient Reinforcement Learning via Posterior Sampling

Reference 27

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source=pdf_text observed=2026-08-10T19:55:35.985031Z digest=sha256:b92128f453d46d588d1ca21c901cded6d12234d977e815f5917a54d59b051ac7

Observation a0d82c19-06b1-48e2-9b55-7a81a83a47d3 · outbound

This paper cites Deep Exploration via Bootstrapped DQN.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Deep Exploration via Bootstrapped DQN

Reference 28

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source=pdf_text observed=2026-08-10T19:55:35.988762Z digest=sha256:6e19e18c835efc98a71a245f8a3a0a79911b3a1fb01c6008a832484431420ee4

Observation c3e4286c-2b98-4250-8750-e9eb24558df7 · outbound

This paper cites Generalization and Exploration via Randomized Value Functions.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Generalization and Exploration via Randomized Value Functions

Reference 29

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source=pdf_text observed=2026-08-10T19:55:35.992509Z digest=sha256:bb591177568377f4a86b8e6c68100460601d62b73a335c5605a6bdf67f7bc792

Observation 47196a34-5709-4e9d-a51d-ca9638fee0d9 · outbound

This paper cites Chen, Xi Chen, T amim Asfour, Pieter Abbeel, and Marcin Andrychowicz.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Chen, Xi Chen, T amim Asfour, Pieter Abbeel, and Marcin Andrychowicz

Reference 30

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:35.996018Z digest=sha256:f9ba50fccd5abd1aad0f5b1cf661df0c9199589d31169bdc503f41851bdf685a

Observation 53751a90-4749-456a-b8e4-023df994e106 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 31

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source=pdf_text observed=2026-08-10T19:55:35.999491Z digest=sha256:6bc5aeb573e2ca2a1c644eb4977645ec19cdb9e35f91de1037e38703ddcfa3ed

Observation 3545d01f-97b2-4f5d-bb40-dede0c232f49 · outbound

This paper cites Proximal Policy Optimization Algorithms.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 32

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source=pdf_text observed=2026-08-10T19:55:36.003778Z digest=sha256:289c3c133b68a98bda17979db3232103eae1c4359e77128c75140fcb2db6e5b2

Observation d9babd3d-aebf-46ab-a1fc-02767ec75d8d · outbound

This paper cites Dropout: A Simple Way to Prevent Neural Networks from Overfitting.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Reference 33

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raw_fallback, observed 2026-08-10T19:55:36.294003Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.007627Z digest=sha256:fa7cb71f0247e808c16d91f3222f7ccf421c21b6ae5ad93f2f6915570136402c

Observation 9b392dea-4d86-489c-961f-efe6e4b9c42a · outbound

This paper cites A Bayesian Framework for Reinforcement Learning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning A Bayesian Framework for Reinforcement Learning

Reference 34

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source=pdf_text observed=2026-08-10T19:55:36.011690Z digest=sha256:3f9516ec3314a1f7ef5760669e5e8d5792202389ff162683a5c1aa5c0f00d626

Observation 3337524b-074e-4167-8d95-62f811177037 · outbound

This paper cites Dyna, an Integrated Architecture for Learning, Planning, and Reacting.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Dyna, an Integrated Architecture for Learning, Planning, and Reacting

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.015240Z digest=sha256:563ca4901a96ca4b30c0a9d9e2ee71d07b86d5dfbc9c13610d0da79b75f86784

Observation ae7ca540-fa42-4f2f-bfd0-c3103dd045a2 · outbound

This paper cites On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.261017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.019116Z digest=sha256:d30c7c316310c57d3ec0fe324bbeb0bb80b9cb9407211fe2d0a91e6a7d32faaf

Observation 56971bb4-63ac-4527-936c-659ef8843b13 · outbound

This paper cites V ariational Inference for the Multi-Armed Contextual Bandit.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning V ariational Inference for the Multi-Armed Contextual Bandit

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.249645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.022895Z digest=sha256:795d1e4dc11ba26f68081baffae38d99feb51336052a008284643968035d8bcb

Observation 04096cd6-3bb0-49d8-aa3c-df0d15ccae3d · outbound

This paper cites When to Use Parametric Models in Reinforcement Learning? In NeurIPS, pages 14322–14333, 2019.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning When to Use Parametric Models in Reinforcement Learning? In NeurIPS, pages 14322–14333, 2019

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.238370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.026409Z digest=sha256:746c44f45db06d3dc66cc1b7626672d983d3d4fa96c81ae38a670edfbbfa5574

Observation ef06e995-8512-4a23-97c7-88d960590991 · outbound

This paper cites Tensor2Tensor for Neural Machine Translation.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Tensor2Tensor for Neural Machine Translation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T19:55:36.029987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:55:36.029987Z digest=sha256:4768b33dddaffab0552612c41eb7b504cf7579e4e062696ea9d067335d0e8321

Observation d32c3623-7c1c-4c12-a132-85d260f028ba · outbound

This paper cites Thompson Sampling via Local Uncertainty.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Thompson Sampling via Local Uncertainty

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.225374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.034436Z digest=sha256:2cdf6cbaa30d04ddc3217ae6f838ad7bee703f240cb77af83afffd685e8945a2

Observation f8dbd2ca-f4a8-4222-bcaa-8bfaf5e3e24d · outbound

This paper cites Model Predictive Path Integral Control using Covariance Variable Importance Sampling.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Model Predictive Path Integral Control using Covariance Variable Importance Sampling

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T19:55:36.038638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:55:36.038638Z digest=sha256:17a2a2abf939098c05213c9ee521d978c77cad84b016efd167a6fe8aa39936a6

Observation 9c55c9e9-4909-43ce-a579-781add8af480 · outbound

This paper cites NADPEx: An On-Policy Temporally Consistent Exploration Method for Deep Reinforcement Learning.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning NADPEx: An On-Policy Temporally Consistent Exploration Method for Deep Reinforcement Learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.213655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.042450Z digest=sha256:7afa11613ca540de4c548bc2190b8a2f592754f876bfc2331121e28641edf003

Observation 7c5363cd-cfff-472a-8eb2-8dfa889e9bfc · outbound

This paper cites Mastering atari games with limited data.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Mastering atari games with limited data

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.201905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.046040Z digest=sha256:256f71cea0c0a0ac6d5cbfa75d52878a2510c1d7c7b875535672648d87ae7c70

Observation 70a78d5d-c334-49f4-8b05-24380d0cd583 · outbound

This paper cites Scalable Thompson Sampling via Optimal Transport.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Scalable Thompson Sampling via Optimal Transport

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.188610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.049728Z digest=sha256:f5510ce8412914be83201ff1b3ae6d2e0af6cb7e41da758f361699d7e647cb60

Observation b9521dd6-d9a3-4c31-9192-6441e53a3706 · outbound

This paper cites Model Based Reinforcement Learning for Atari.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Model Based Reinforcement Learning for Atari

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:55:36.176782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T19:55:36.053274Z digest=sha256:b160f4bb627205a019f601659de27f5eee440a3330e938752fafcef6f397d6b0

Observation 0ee6a3ed-584e-472c-80d0-afbc07236faa · outbound

This paper cites Prioritized Sequence Experience Replay.

EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning Prioritized Sequence Experience Replay

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-10T19:55:35.912989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T19:55:35.912989Z digest=sha256:ec8a312b8d9dc62873eeb7791de21e37964a8a5f5308fbfb1f66728ee74ec507

Pith citing papers

No inbound Pith citation observations are available.