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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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

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  • verified fuzzy0
  • unresolved30
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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

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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

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

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

Reference 9

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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

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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

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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

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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

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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

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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

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

Reference 21

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

Reference 36

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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

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

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

Reference 1997

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

Reference 2010

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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

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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

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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

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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

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

Reference 2018

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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

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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

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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

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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

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

Reference 2024

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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

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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

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Pith citing papers

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