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Paper Citation Record · LEDGER

Iterative variational learning of committor-consistent transition pathways using artificial neural networks

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.01947.

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

pith.paper-citation-record.v1
2412.01947 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:08:50.549036Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

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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

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 56ad83c5-c64a-4eaf-b7d2-126ec0652f93 · outbound

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 1

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Observation f22ab872-0e47-470a-b901-3db7ffea5eae · outbound

This paper cites The Journal of Chemical Physics 109(6), 2325–2338 (1998) https://doi.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks The Journal of Chemical Physics 109(6), 2325–2338 (1998) https://doi

Reference 2

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This paper cites Biophysical Journal 80(1), 12–30 (2001) https: //doi.org/10.1016/S0006-3495(01)75992-9.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks Biophysical Journal 80(1), 12–30 (2001) https: //doi.org/10.1016/S0006-3495(01)75992-9

Reference 3

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This paper cites The Journal of Chemical Physics 152(14), 144102 (2020) https://doi.org/10.1063/5.0004838.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks The Journal of Chemical Physics 152(14), 144102 (2020) https://doi.org/10.1063/5.0004838

Reference 4

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 5

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 6

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 7

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This paper cites On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 8

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 9

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This paper cites In: Bengio, Y., LeCun, Y.

Iterative variational learning of committor-consistent transition pathways using artificial neural networks In: Bengio, Y., LeCun, Y

Reference 10

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 11

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

Reference 12

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Multiscale Model

Reference 16

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This paper cites IEEE transactions on information theory 28(2), 129–137 (1982).

Iterative variational learning of committor-consistent transition pathways using artificial neural networks IEEE transactions on information theory 28(2), 129–137 (1982)

Reference 17

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Expert Syst

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Unresolved cited work

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks https://github.com/jeffcomer/ DiffusionFusion

Reference 33

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Iterative variational learning of committor-consistent transition pathways using artificial neural networks Science 334(6055), 517–520 (2011) https://doi.org/10.1126/science

Reference 37

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Pith citing papers

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