Paper Citation Record · LEDGER

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2512.04223.

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

pith.paper-citation-record.v1
2512.04223 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:40:30.963090Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

35 of 35 outbound references displayed

verified exact4
verified fuzzy0
unresolved30
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f648d863-84c1-45af-9b6b-e63bc5a8cf28 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Asso- ciates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Asso- ciates, Inc

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.404204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 045311ef-e673-4e42-b1f8-5a190cec1f20 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.751955Z

Source-reported events for the cited work

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Observation 3a4b52fc-13e3-4014-963f-2a48da5049fa · outbound

This paper cites an unresolved cited work.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.830532Z

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Observation caf75951-eafb-469d-b005-659ff153be4b · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.064209Z

Source-reported events for the cited work

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Observation 64c5cf66-55af-46a2-b85e-3d542174b1b0 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Categorical Reparameterization with Gumbel-Softmax

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.244962Z

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Observation 88685a6e-b242-4815-8572-6bf88a0b54bb · outbound

This paper cites Neurocomputing 606, 128360.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Neurocomputing 606, 128360

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.366634Z

Source-reported events for the cited work

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Observation bbd0c2fc-7cef-4a31-addf-e5358c5d1f40 · outbound

This paper cites Transportation Research Part C: Emerging Technologies 137, 103616.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Transportation Research Part C: Emerging Technologies 137, 103616

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.587380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c63da2c-5c75-4d21-940f-a712857fe10f · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Cur- ran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Cur- ran Associates, Inc

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.713015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 535be0d8-cb66-48ba-9ae4-edcd7a2a0b3e · outbound

This paper cites Transportation Research Record 2677, 1–23.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Transportation Research Record 2677, 1–23

Reference 20

Resolution: verified exact; doi, observed 2026-08-03T18:43:30.326458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f6e2c477-2e08-4aab-ada0-fb18d75f1663 · outbound

This paper cites an unresolved cited work.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.888563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab8821c2-49f5-4874-9657-235bec149c9f · outbound

This paper cites International Journal of Geographical Information Science 38, 407–431.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach International Journal of Geographical Information Science 38, 407–431

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.935994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5baa6790-90fe-4992-9f1e-f2530f66f30f · outbound

This paper cites Travel Behaviour and Soci- ety 32, 100595.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Travel Behaviour and Soci- ety 32, 100595

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.123168Z

Source-reported events for the cited work

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Observation eeebb129-df2b-4dae-8d03-c0baf917d18f · outbound

This paper cites Conditional Image Generation with PixelCNN Decoders.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Conditional Image Generation with PixelCNN Decoders

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.209134Z

Source-reported events for the cited work

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Observation 8d542a81-9db6-4d88-9e6f-4764251c8942 · outbound

This paper cites Transportation Research Part C: Emerging Technologies 155, 104291.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Transportation Research Part C: Emerging Technologies 155, 104291

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.335948Z

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Unavailable: canonical work link unavailable.

Observation 6d7fb6a5-5948-49c9-8ae1-e4ef2210c694 · outbound

This paper cites (Eds.), Proceedings of the 31st International Conference on Machine Learning, PMLR, Bejing, China.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Proceedings of the 31st International Conference on Machine Learning, PMLR, Bejing, China

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.440573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4e1448b-1059-4e67-9edb-3f291b8e7649 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.554452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 149e6baa-5346-4df8-b7d2-da9f3cf86fae · outbound

This paper cites Transportation Research Part C: Emerging Technologies 179, 105273.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Transportation Research Part C: Emerging Technologies 179, 105273

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.751587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb8a2473-40cd-41bf-a352-c1738184ffbf · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.830762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d282b275-5b5f-4ff8-a96f-5552e414e3e5 · outbound

This paper cites Improved Vector Quantized Diffusion Models.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Improved Vector Quantized Diffusion Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.917604Z

Source-reported events for the cited work

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Observation 9ad58ef2-14e0-4631-94d9-b83ded7117f5 · outbound

This paper cites 1316–1324.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach 1316–1324

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.963090Z

Source-reported events for the cited work

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Observation 77c0fe6d-9ddf-464f-9758-c2ff1fedbe79 · outbound

This paper cites Nayak, S., Pandit, D.,.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Nayak, S., Pandit, D.,

Reference 57

Resolution: verified exact; doi, observed 2026-08-03T18:43:30.111932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51acc4eb-52b1-496d-adfa-961ba1d9dd05 · outbound

This paper cites Neural Computation 9, 1735–1780.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Neural Computation 9, 1735–1780

Reference 1997

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.133167Z

Source-reported events for the cited work

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Observation f484f881-8e4f-44ed-bae2-ec9adac20788 · outbound

This paper cites Journal of Choice Modelling 3, 5–31.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Journal of Choice Modelling 3, 5–31

Reference 2010

Resolution: verified exact; doi, observed 2026-08-03T18:43:30.552180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3f03ff55-8cbb-4272-97dc-635031e4b744 · outbound

This paper cites Auto-Encoding Variational Bayes.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Auto-Encoding Variational Bayes

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:29.792177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56b9be68-72a3-460a-9591-1edd007820c5 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.883201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13ee4d0f-8cb0-48f9-918d-18b421f51d5a · outbound

This paper cites (Eds.), Advances in Neural Information Process- ing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Process- ing Systems, Curran Associates, Inc

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.664229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b58db38-a56e-4e30-808c-a29762d9a438 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.147383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b48a20-8e88-419d-bc70-e4cdecf6c862 · outbound

This paper cites Understanding disentangling in $\beta$-VAE.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Understanding disentangling in $\beta$-VAE

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.501449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3a4fc31-ced4-47d9-846a-ee3054e793a3 · outbound

This paper cites (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:30.042670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb794e7d-9fb1-4351-afeb-2fb3df4f21dc · outbound

This paper cites A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.078763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a06e030c-5676-48db-848e-37f59849b49f · outbound

This paper cites Transportation Research Part C: Emerging Technolo- gies 123, 102972.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Transportation Research Part C: Emerging Technolo- gies 123, 102972

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.986980Z

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Unavailable: canonical work link unavailable.

Observation d493c7b9-6279-4f56-b513-52e8c999462a · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7327–7347.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7327–7347

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:28.201630Z

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Observation ee6f721c-9a9b-4bf6-a773-e1331b19f8c9 · outbound

This paper cites 1886–1890.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach 1886–1890

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T18:40:27.980077Z

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Observation 32e34973-77be-4d18-81fb-a7c6fa2927d3 · outbound

This paper cites URL:https:// www.sciencedirect.com/science/article/pii/S0968090X24004182, doi:https://doi.org/10.1016/ j.trc.2024.104897.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach URL:https:// www.sciencedirect.com/science/article/pii/S0968090X24004182, doi:https://doi.org/10.1016/ j.trc.2024.104897

Reference 2025

Resolution: malformed identifier; no resolver link, observed 2026-08-03T18:40:30.651997Z

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Observation f4c537ef-7b5a-4d01-be4e-eba831564a68 · outbound

This paper cites Shone, F., Hillel, T.,.

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach Shone, F., Hillel, T.,

Reference 6097

Resolution: verified exact; doi, observed 2026-08-03T18:43:29.862778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.