Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:44.749058Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 100 of 294 outbound references and 0 inbound Pith citation observations for arXiv:2608.07870.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:44.749058Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 294 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e57fe484-753a-4801-94ee-3ff9606f0877 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Reinforcement Learning Conference , year=
Reference 1
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Observation 0a1769ee-b33b-472d-bce1-c6040c873083 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-first International Conference on Machine Learning , year=
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Observation f566992d-9f2a-4076-a641-251a09bb0f24 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 1995 , publisher=
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Observation 6fe540d3-b1d3-44f6-8415-3847da813fe3 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Nature , volume=
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Observation 05dafc2f-1811-4649-9c9c-2d5770715ac2 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Computing in science & engineering , volume=
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Observation c76a30be-da7f-48e6-a214-3974facf7d31 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work
Reference 6
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Observation 6b68cf87-fb4b-4cf4-a1d3-0cb5b3867532 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control , journal=
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Observation 2ce24967-37c6-4a39-afae-8b685bd9e7fe · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control IOS Press , year = 2016, pages =
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Observation 537cbd8a-edd5-4e73-b8dc-77b7344564ac · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Python for Data Analysis: Data Wrangling with Pandas,
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Observation 88ca4713-8e5c-451e-bdb2-b82c241a8bd2 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-second International Conference on Machine Learning , year=
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Observation ba8fa836-86f2-4c74-8f27-38838dfbb0eb · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Thirteenth International Conference on Learning Representations , year=
Reference 11
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Observation 5307efec-b126-4005-ba32-fc25dc5c5554 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Mixture of Experts in a Mixture of
Reference 12
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Observation bad3e48c-f9ca-41f6-b3e6-e3fecc873e94 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Forty-third International Conference on Machine Learning , year=
Reference 13
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Observation 0a8446cb-4ed2-45e6-bb9b-e499db6719b4 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=
Reference 14
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Observation 5150b72d-6b65-4fd6-98e3-f72911551c87 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 15
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Observation 88367737-f250-4c95-9ced-7c15ca0d2c37 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control A Survey of State Representation Learning for Deep Reinforcement Learning
Reference 16
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Observation e44d22c7-a2b9-4b68-bb0d-17eb3c6e61f3 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Learning Representations , volume=
Reference 17
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Observation 11ba5a13-27d9-4a8a-9098-e9b6fbb2c8b6 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Reference 18
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Observation f100fbb2-efc4-4b6c-a740-d696d3c323c9 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work
Reference 19
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Observation 822fcae4-438d-4c94-ba91-80311edbadab · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 20
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Observation add1ad3c-3f92-43de-9a1f-3a94bc49de80 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 21
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Observation 01506ef6-e2e9-4d3c-bb21-c04ad0be6a5d · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Towards General-Purpose Model-Free Reinforcement Learning
Reference 22
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Observation e823bf09-6b87-401b-a16f-93f22e1f97e0 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Data-Efficient Reinforcement Learning with Self-Predictive Representations
Reference 23
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Observation 659b241e-5954-401b-b281-00999289429d · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 24
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Observation 588fb875-6a1a-40b6-b008-c2be8e4ddcc3 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS) , pages=
Reference 25
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Observation f08bcd87-10c5-41df-8e2a-a31162099b81 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 26
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Observation f6d33a24-ca73-4944-a4ef-b070bf066548 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 27
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Observation aacf2e18-d1af-4451-8395-b794e5fe600f · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 28
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Observation fcf7acd0-e71d-4f35-9770-c83b315843e7 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 29
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Observation fa20d1a6-60b3-4c7d-bb46-db45444c0afd · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=
Reference 30
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Observation 740f8dde-456b-467f-97e5-c227892f50db · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Reference 31
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Observation 191fbf1a-a53a-4466-8534-fd926951d195 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=
Reference 32
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Observation a702cc7e-7664-411c-a733-6e62d9961d49 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control , author=
Reference 33
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Observation 66e176ce-c77d-4edd-a955-5227dad8bba4 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Bridging State and History Representations: Understanding Self-Predictive RL
Reference 34
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Observation 5974cec4-da9d-49dd-b719-f69b3a4ea8c8 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint
Reference 35
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Observation a21eed42-d0d2-43af-906c-fadf49141fc7 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Conference on robot learning , pages=
Reference 36
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Observation e74c4449-ae73-48dc-9134-5bedfa202383 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control World Models
Reference 37
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Observation 05453046-1d4f-4fed-baa2-6bf8004e17e1 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2016 IEEE International Conference on Robotics and Automation (ICRA) , pages=
Reference 38
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Observation fdbb37c7-128f-4300-99a0-637a8505ac81 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=
Reference 39
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Observation 6346b7e8-e270-4e32-8b59-a358ed14bec9 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=
Reference 40
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Observation ba4fd107-1f6d-4719-9e68-1681dc3207e4 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=
Reference 41
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Observation df9dfe30-75cb-421d-9aa3-6b57b8d080bc · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Reference 42
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Observation 8a59a100-b319-49e4-a15c-c3632c901f6c · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 43
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Observation 866e7b91-cd58-4b2b-a212-1903425b48c6 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 44
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Observation 7de685ac-596f-42f6-8523-7251ead4d9af · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Never Give Up: Learning Directed Exploration Strategies
Reference 45
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Observation a5fc1bad-1600-422a-b509-cbab437ef210 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 46
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Observation c938ee00-49a0-4d3f-9224-54bdcfd536ce · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
Reference 47
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Observation a89cd13f-7dd9-4fb9-908d-3ed70fd1b6f9 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control nature , volume=
Reference 48
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Observation 1d7635b6-8253-4f0e-a4db-b7e2aaba4979 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the aaai conference on artificial intelligence , volume=
Reference 49
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Observation a9715d7f-6d3d-4154-b07d-d133c6410890 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
Reference 50
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Observation 0787e997-171a-42a8-bbc5-fb5348ab050a · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Reference 51
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Observation 5a1f2ae1-7053-4aa4-8249-1cf7dd2ab0c7 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
Reference 52
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Observation a7848cbe-36cc-4fd7-b152-8a881c73140c · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of Thirty Third Conference on Learning Theory , pages =
Reference 53
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Observation 9ad6e267-1e30-4cd5-afe6-1f372f6bf4a4 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of Thirty Third Conference on Learning Theory , pages =
Reference 54
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Observation a1dac091-19f1-4d9f-935e-403de56169eb · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 55
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Observation 481ad074-4492-4aec-af65-831112326ca8 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 56
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Observation f7b6e6e6-1e94-46b4-8bd9-b07cfae3ea6e · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2022 , url=
Reference 57
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Observation aa9f190a-1632-4f81-a78a-6eedaf61126d · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Learning Representations , year=
Reference 58
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Observation 7159aae7-50a7-4cca-8517-d2148e9c84d2 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the 36th International Conference on Machine Learning , pages =
Reference 59
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Observation 88b5cb1d-64b3-4d44-ac5b-b5468ef11ab0 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Reference 60
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Observation 7db6e75c-242c-4144-bafb-a2ad44777109 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=
Reference 61
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Observation ae02036d-b966-4feb-a596-1ea638cd1e4e · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Transient Non-Stationarity and Generalisation in Deep Reinforcement Learning
Reference 62
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Observation a694a16d-40ef-4fa2-a5a3-3939c02c198e · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Reference 63
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Observation 5f3d7c36-6692-421e-8af4-341ce0394667 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Dual PatchNorm
Reference 64
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Observation 6e5d77ba-9bca-45a6-9596-99d4cc7545b6 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Neural Networks: Tricks of the trade , pages=
Reference 65
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Observation 89892540-148f-41c9-8199-3c3757fef08d · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in neural information processing systems , volume=
Reference 66
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Observation 656a39ed-5baf-4ff7-ae54-47192e079f5f · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Neural computation , volume=
Reference 67
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Observation eec08843-19a2-4d19-abf2-c7bcd0c556bc · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE international conference on computer vision , pages=
Reference 68
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Observation 7a978f6a-975e-4358-b266-4072dd8a47da · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Advances in Neural Information Processing Systems , volume=
Reference 69
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Observation 5c1560b4-eaec-41f4-9ffd-8313f288d136 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work
Reference 70
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Observation 092922f2-095b-4ee8-8015-1802f4ca0e8c · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work
Reference 71
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Observation 4a6eba36-c76c-475c-baf8-4355bc403b0b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Loss of Plasticity in Continual Deep Reinforcement Learning
Reference 72
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Observation 3083b5e7-f1ff-4425-bf69-49df7a39c65b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control international conference on machine learning , pages=
Reference 73
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Observation cc5631e0-0328-4c37-8697-398b072ed5b0 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Reference 74
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Observation f1048067-a307-4dd2-aefb-2b27646ef0d1 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Unresolved cited work
Reference 75
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Observation ac1316a7-b1ff-449e-87d7-9f2ebf58db65 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Reference 76
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Observation 48e6131f-5c0d-4be8-8b2f-04f73cc9ef8e · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Twelfth International Conference on Learning Representations , year=
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Observation 3621492d-7eb3-494d-ac9d-af93697f62c6 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control The Eleventh International Conference on Learning Representations , year=
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Observation e74d3c0f-1377-4b60-bc1c-b54ecebacb77 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=
Reference 79
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Observation 8c078f70-f851-4f6d-ba70-56c10cbd7c74 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Maintaining Plasticity in Continual Learning via Regenerative Regularization
Reference 80
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Observation b1338b8b-881d-4944-bd49-7cc6e514922c · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Maintaining Plasticity in Deep Continual Learning
Reference 81
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Observation a540b251-0436-4bb3-b40a-c6a3b011decd · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Directions of Curvature as an Explanation for Loss of Plasticity
Reference 82
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Observation 42e671c6-30d9-48c4-a982-7ee30ad37c4b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Continual Learning as Computationally Constrained Reinforcement Learning
Reference 83
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Observation 5b658bb8-0a54-45f2-bffb-f9ae7a1703dd · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=
Reference 84
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Observation 939ebcf0-c446-41c6-addd-c322d521372b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Fantastic Generalization Measures and Where to Find Them
Reference 85
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Observation e431a621-eb13-49e9-b1ca-0682e4555641 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International Conference on Machine Learning , pages=
Reference 86
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Observation 03aa83d4-fd71-4c1d-b067-36639e57df63 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Very Deep Convolutional Networks for Large-Scale Image Recognition
Reference 87
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Observation 01a53970-1303-4af8-9233-dd8b5f0e2875 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
Reference 88
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Observation 0733bf70-d8c7-4548-952e-dc4d006018cb · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 89
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Observation c9b395ce-24c1-40c4-8e06-7e113214e74f · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control GPT-4 Technical Report
Reference 90
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Observation ce4d376a-584b-4940-b510-3a5af02928f5 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Gemini: A Family of Highly Capable Multimodal Models
Reference 91
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Observation 250fcb3d-91fb-4ccb-9fa3-0cf71b0967b9 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control 2009 , institution=
Reference 92
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Observation 11008eb8-e879-48f1-a295-7a029984a27e · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control http://yann.lecun.com/exdb/mnist/ , year=
Reference 93
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Observation 171825c9-837a-4b80-bdd9-d3643d880535 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Decoupled Weight Decay Regularization
Reference 94
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Observation 57338cf0-9981-44cc-baf3-464c9f27ba0d · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Reference 95
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Observation da8aa900-e90f-42fa-ae55-5bb26bb1c785 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 96
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Observation e41de224-c6e4-4d61-96a2-cfd42fd61d95 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Layer Normalization
Reference 97
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Observation 2ee77a5a-2619-40d5-a0fd-0aa89e696a3b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Resetting the Optimizer in Deep RL: An Empirical Study
Reference 98
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Observation c087bb13-2e33-4eb3-9917-a72127cbb8a4 · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International conference on machine learning , pages=
Reference 99
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Observation 99a9f70a-aaba-457c-88c4-3832fdb5da5b · outbound
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control International journal of computer vision , volume=
Reference 100
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No inbound Pith citation observations are available.