Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition | Paper Citation Record

Observation b92fcb25-15cf-4190-81cb-2ecc009e3486 · outbound

This paper cites Controlling machine-learning algorithms and their biases.McKin- sey Insights, 2017.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Controlling machine-learning algorithms and their biases.McKin- sey Insights, 2017

Reference 1

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Observation 197c0f22-de69-473b-97cb-d6e6eb8dad56 · outbound

This paper cites Determining what individual sus scores mean: Adding an adjective rating scale.Journal of usability studies, 4(3):114–123, 2009.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Determining what individual sus scores mean: Adding an adjective rating scale.Journal of usability studies, 4(3):114–123, 2009

Reference 2

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This paper cites Morgan & Claypool Publishers, 2016.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Morgan & Claypool Publishers, 2016

Reference 3

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This paper cites Human-in-the-loop techniques in machine learning.IEEE Data Eng.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Human-in-the-loop techniques in machine learning.IEEE Data Eng

Reference 4

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This paper cites an unresolved cited work.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Unresolved cited work

Reference 5

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Observation de5e19b2-7026-495d-ad65-09a6a9e8b83a · outbound

This paper cites Human-ai ensembles: When can they work?Journal of Management, 51(2):536–569, 2025.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Human-ai ensembles: When can they work?Journal of Management, 51(2):536–569, 2025

Reference 6

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Observation af583cf9-8981-4591-9a80-cf7c3e97b766 · outbound

This paper cites User modelling for avoiding overfitting in interactive knowledge elicitation for prediction.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition User modelling for avoiding overfitting in interactive knowledge elicitation for prediction

Reference 7

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Observation 5dc99786-4f0d-496d-9fcc-efa875cc175d · outbound

This paper cites Bold: Dataset and metrics for measuring biases in open-ended language generation.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Bold: Dataset and metrics for measuring biases in open-ended language generation

Reference 8

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Observation c2f237d6-a470-4a07-ba48-8aac3bf22e35 · outbound

This paper cites Interactive machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Interactive machine learning

Reference 9

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Observation cd7bf3a0-b57f-4818-a771-147e0bb627f6 · outbound

This paper cites Latent inspector: An interactive tool for probing neural network behaviors through arbitrary latent activation.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Latent inspector: An interactive tool for probing neural network behaviors through arbitrary latent activation

Reference 10

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Observation dc0a58ea-00e2-4b0d-af36-ff5082f59d0a · outbound

This paper cites Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition

Reference 11

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Observation 15dca80c-6f7f-484b-ae92-68feb6322856 · outbound

This paper cites Towards human-guided machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Towards human-guided machine learning

Reference 12

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Observation 6e82918c-ca13-4d17-b7e5-0aee353630f5 · outbound

This paper cites Explain- ing explanations: An overview of interpretability of machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Explain- ing explanations: An overview of interpretability of machine learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.360583Z

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This paper cites A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.350501Z

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This paper cites Nasa-task load index (nasa-tlx); 20 years later.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Nasa-task load index (nasa-tlx); 20 years later

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.339522Z

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Observation 6a56a6ed-65b0-4b90-b870-2e85194101d3 · outbound

This paper cites Hart and Lowell E.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Hart and Lowell E

Reference 16

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Observation 58a1ef56-02dd-49fa-8b95-404431f470fc · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Distilling the Knowledge in a Neural Network

Reference 17

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Observation 7e4a9e73-47b9-4e7a-b696-f9c86a851ec5 · outbound

This paper cites Interactive machine learning for health informatics: when do we need the human- in-the-loop?Brain informatics, 3(2):119–131, 2016.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Interactive machine learning for health informatics: when do we need the human- in-the-loop?Brain informatics, 3(2):119–131, 2016

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.317629Z

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Observation 436703a0-d04a-4fc9-8a62-32cba1b96542 · outbound

This paper cites An empirical evaluation of predicted outcomes as explanations in human-ai decision-making.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition An empirical evaluation of predicted outcomes as explanations in human-ai decision-making

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.307065Z

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Observation 02f89f25-f8f4-4297-9fed-33f1d9eadc67 · outbound

This paper cites Heinrich.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Heinrich

Reference 20

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Observation 024efb7f-1e76-4b6b-96f6-9b01b121dd18 · outbound

This paper cites Studying the Transfer of Biases from Programmers to Programs.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Studying the Transfer of Biases from Programmers to Programs

Reference 21

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This paper cites In- terpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition In- terpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 22

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Observation 6035892a-e50c-4cc4-ad6a-ff91cd6e6060 · outbound

This paper cites Learning multiple layers of features from tiny images.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Learning multiple layers of features from tiny images

Reference 23

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Observation fe5dc7f8-5583-4a79-8b8c-eaaedb48e3d2 · outbound

This paper cites LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions

Reference 24

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Observation 90c7ec48-e32d-49c1-9db1-106c6282af26 · outbound

This paper cites Principles of explanatory de- bugging to personalize interactive machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Principles of explanatory de- bugging to personalize interactive machine learning

Reference 25

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Observation adff6faf-3447-4921-b71d-c95a85f56109 · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Deep learning.nature, 521(7553):436–444, 2015

Reference 26

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Observation e2f61d51-72a6-45a8-ac96-80ed9ce15fa8 · outbound

This paper cites Umux-lite: when there’s no time for the sus.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Umux-lite: when there’s no time for the sus

Reference 27

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Observation 8fa637c4-4280-485d-a940-25eb82cea4da · outbound

This paper cites Investigating the correspondence between umux- lite and sus scores.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Investigating the correspondence between umux- lite and sus scores

Reference 28

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Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Unresolved cited work

Reference 29

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This paper cites The mythos of model interpretability: In machine learning, the concept of inter- pretability is both important and slippery.Queue, 16(3):31–57, 2018.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition The mythos of model interpretability: In machine learning, the concept of inter- pretability is both important and slippery.Queue, 16(3):31–57, 2018

Reference 30

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Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Unresolved cited work

Reference 31

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Observation 42d9af8e-a6f0-40ad-ae5c-32889527a744 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 32

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Observation b0c3f90d-aa82-4fbd-a03b-8a04d6c388d0 · outbound

This paper cites Simon and Schuster, 2021.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Simon and Schuster, 2021

Reference 33

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Observation 9c6ec203-d462-4a81-92d4-5c7fabad6495 · outbound

This paper cites Du, Eunyee Koh, and T.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Du, Eunyee Koh, and T

Reference 34

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Observation 0dd690b8-7c0c-4329-971b-7c48b599c2a7 · outbound

This paper cites Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems

Reference 35

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Pytorch: An imperative style, high-performance deep learning library

Reference 36

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Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Unresolved cited work

Reference 37

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Observation a4a62295-fceb-4eef-b3e5-e47c158c70f9 · outbound

This paper cites Robust speech recognition via large-scale weak supervision, 2022.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Robust speech recognition via large-scale weak supervision, 2022

Reference 38

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This paper cites Introducing a new benchmarked dataset for activity monitoring.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Introducing a new benchmarked dataset for activity monitoring

Reference 39

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Observation f3effaa3-8585-4dcc-9443-c9cfbc7793e2 · outbound

This paper cites ” why should i trust you?” explaining the predictions of any classifier.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition ” why should i trust you?” explaining the predictions of any classifier

Reference 40

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This paper cites Human-ai collaboration: Exploring interfaces for interactive machine learning.Tuijin Jishu/Journal of Propulsion Technology, 44(2):2023, 2023.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Human-ai collaboration: Exploring interfaces for interactive machine learning.Tuijin Jishu/Journal of Propulsion Technology, 44(2):2023, 2023

Reference 41

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Observation 50fd4b35-f80d-4e2b-8963-0f441a44e643 · outbound

This paper cites Visual integration of model and data spaces in classification problems.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Visual integration of model and data spaces in classification problems

Reference 42

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This paper cites Active learning literature survey.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Active learning literature survey

Reference 43

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This paper cites Are bias mitigation techniques for deep learning effective?arXiv e-prints, pages arXiv–2104, 2021.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Are bias mitigation techniques for deep learning effective?arXiv e-prints, pages arXiv–2104, 2021

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.112778Z

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This paper cites / HILL: Interactively Guiding Model Training Through Human Intuition Erin Sullivan, and Jonathan Herlocker.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition / HILL: Interactively Guiding Model Training Through Human Intuition Erin Sullivan, and Jonathan Herlocker

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.101914Z

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Observation b2528b5c-05e7-4e92-b110-f15325a2dce5 · outbound

This paper cites Evolution and impact of bias in human and machine learning algorithm interaction.Plos one, 15(8):e0235502, 2020.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Evolution and impact of bias in human and machine learning algorithm interaction.Plos one, 15(8):e0235502, 2020

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.091073Z

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This paper cites Explanatory interactive machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Explanatory interactive machine learning

Reference 47

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This paper cites Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Visualizing data using t-sne.Journal of machine learning research, 9(11), 2008

Reference 48

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This paper cites An interactive approach to bias mitigation in machine learning.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition An interactive approach to bias mitigation in machine learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.066535Z

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Observation 0d8defa0-2e22-4599-ba18-f050811c0b70 · outbound

This paper cites SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:58:57.949838Z

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

Observation 7259be34-de97-47cd-a147-3caeb3147ad5 · outbound

This paper cites Evaluating the promise of human-algorithm collaborations in everyday work practices.Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–23, 2019.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Evaluating the promise of human-algorithm collaborations in everyday work practices.Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–23, 2019

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.055449Z

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.

Observation 347db077-8503-4c13-b0c8-293efe85c9b1 · outbound

This paper cites Acceler- ating human-in-the-loop machine learning: Challenges and opportunities.

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition Acceler- ating human-in-the-loop machine learning: Challenges and opportunities

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:58:58.043897Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers