Paper Citation Record · LEDGER

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2412.08507.

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

pith.paper-citation-record.v1
2412.08507 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:46:47.327709Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:58:57.767017Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T22:58:58.026974Z

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy15
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e4ba62a-b323-4700-9cc9-6cdaf940c9f0 · outbound

This paper cites A survey on human activity recognition using wearable sensors,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition A survey on human activity recognition using wearable sensors,

Reference 1

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Source-reported events for the cited work

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Observation d584b154-d8da-499a-8737-b24dc3606bb5 · outbound

This paper cites Deep learning for sensor-based activity recognition: A survey,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Deep learning for sensor-based activity recognition: A survey,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T17:46:47.140961Z

Source-reported events for the cited work

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Observation e76ad806-208a-4357-8290-827e32d95a67 · outbound

This paper cites Activity recognition us- ing cell phone accelerometers,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Activity recognition us- ing cell phone accelerometers,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.985202Z

Source-reported events for the cited work

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Observation 87d9bbfb-7c39-4b53-872d-361ed7371b1e · outbound

This paper cites imove: Exploring bio-impedance sensing for fitness activity recogni- tion,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition imove: Exploring bio-impedance sensing for fitness activity recogni- tion,

Reference 4

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Observation efc95809-4392-4c61-a2a8-71f509dd1d0f · outbound

This paper cites Embedding textile capacitive sensing into smart wearables as a versatile solution for human motion capturing,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Embedding textile capacitive sensing into smart wearables as a versatile solution for human motion capturing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.947924Z

Source-reported events for the cited work

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Observation 9e7bac83-8b68-4da2-8c25-8bb8b9bc5f8d · outbound

This paper cites The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T17:46:47.170504Z

Source-reported events for the cited work

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Observation 3ae9c5fe-bbf5-4cee-b112-6ef78186abd0 · outbound

This paper cites A survey of methods for explaining black box models,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition A survey of methods for explaining black box models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.909292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3bed1c67-b74a-4c92-9f9e-78e764948255 · outbound

This paper cites ” why should i trust you?.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition ” why should i trust you?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T17:46:47.185004Z

Source-reported events for the cited work

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Observation 20e616b7-f2d9-4f73-b760-966572965bd6 · outbound

This paper cites The Power of Training: How Different Neural Network Setups Influence the Energy Demand.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition The Power of Training: How Different Neural Network Setups Influence the Energy Demand

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:46:47.497277Z

Source-reported events for the cited work

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Observation eb45a585-ff35-4e8b-be80-e4cb96de1a79 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Towards A Rigorous Science of Interpretable Machine Learning

Reference 10

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Source-reported events for the cited work

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Observation 6e9c929e-f617-4f6b-9540-37fa6bf2c9fc · outbound

This paper cites Interactive slice visualiza- tion for exploring machine learning models,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Interactive slice visualiza- tion for exploring machine learning models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.872524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8c728ae8-1f01-4163-8d73-4bb9386d57c8 · outbound

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

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Latent inspector: an interactive tool for probing neural network behaviors through arbitrary latent activation,

Reference 12

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Source-reported events for the cited work

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Observation e98aa0a1-fa7b-4cda-b890-afcdf369270c · outbound

This paper cites Visualizing dataflow graphs of deep learning models in tensorflow,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Visualizing dataflow graphs of deep learning models in tensorflow,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.821888Z

Source-reported events for the cited work

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Observation 6d60d628-f86b-4cb6-bf86-565a883e4cfe · outbound

This paper cites Visualizing the hidden activity of artificial neural networks,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Visualizing the hidden activity of artificial neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.796473Z

Source-reported events for the cited work

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Observation ebbacfeb-962d-4a41-97b2-69066f9ee489 · outbound

This paper cites Visual human+machine learn- ing,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Visual human+machine learn- ing,

Reference 15

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Source-reported events for the cited work

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Observation 5423edcb-a378-4563-8aaf-b678676c84a7 · outbound

This paper cites What you see is what you can change: Human- centered machine learning by interactive visualization,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition What you see is what you can change: Human- centered machine learning by interactive visualization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.754689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8419109a-914a-42d6-b5f7-22c4883b16c8 · outbound

This paper cites Fine-tuning deep neural networks by interactively refining the 2d latent space of ambiguous images.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Fine-tuning deep neural networks by interactively refining the 2d latent space of ambiguous images

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08032ef1-47cf-4b4b-a3b7-65a337618998 · outbound

This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Infogan: Interpretable representation learning by information maximizing generative adversarial nets,

Reference 18

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Observation 5729423d-c513-4cb6-a4f6-e8ba6da628fc · outbound

This paper cites Ib-gan: Disentangled rep- resentation learning with information bottleneck generative adversarial networks,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Ib-gan: Disentangled rep- resentation learning with information bottleneck generative adversarial networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.698217Z

Source-reported events for the cited work

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Observation bdb6e2f4-6cdf-4bf7-acbe-69690bc337fe · outbound

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

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Understanding disentangling in $\beta$-VAE

Reference 20

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Source-reported events for the cited work

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Observation 3979add3-642e-4f16-b8ef-fa476e764bfa · outbound

This paper cites Disen- tanglement via latent quantization,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Disen- tanglement via latent quantization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.672024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d7be270d-18d7-4613-a663-bcccdcd708f2 · outbound

This paper cites Human-interpretable model explainability on high-dimensional data.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Human-interpretable model explainability on high-dimensional data

Reference 22

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Observation e1ce65d8-9e97-4514-82af-a57fa3b023c8 · outbound

This paper cites A disentangling invertible interpretation network for explaining latent representations,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition A disentangling invertible interpretation network for explaining latent representations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.647332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 18fea037-a06b-4636-8faf-bc9c4f101364 · outbound

This paper cites This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks

Reference 24

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Observation 80a712d9-0975-447c-8bfe-01d01325fe25 · outbound

This paper cites A survey on neural network interpretability,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition A survey on neural network interpretability,

Reference 25

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Source-reported events for the cited work

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Observation 3e7f6bfb-38e8-4c88-9ee7-aad1dfaec153 · outbound

This paper cites Explaining deep neural networks and beyond: A review of methods and applications,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Explaining deep neural networks and beyond: A review of methods and applications,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.608935Z

Source-reported events for the cited work

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Observation 14735d58-ac10-42a7-b90f-72e19f113e77 · outbound

This paper cites Interpretable deep learning: Interpretation, interpretability, trustworthi- ness, and beyond,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Interpretable deep learning: Interpretation, interpretability, trustworthi- ness, and beyond,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:46:47.584850Z

Source-reported events for the cited work

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Observation bf50746b-ee6f-4a91-a836-d6a4e056a2ba · outbound

This paper cites Liii. on lines and planes of closest fit to systems of points in space,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Liii. on lines and planes of closest fit to systems of points in space,

Reference 28

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Observation b962cd76-87bb-4cae-9908-8b5c1667ec76 · outbound

This paper cites Visualizing data using t-sne.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Visualizing data using t-sne

Reference 29

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Observation 5bea378d-45f9-4582-81b3-af6472b8d51c · outbound

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

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 30

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Observation dd0d147f-78d6-4bb5-aad9-a3a8ed198f4f · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Learning transferable visual models from natural language supervision,

Reference 31

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Observation b22e0dee-7579-44f2-a4a5-2466cf761fb2 · outbound

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

Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition Introducing a new benchmarked dataset for activity monitoring,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T17:46:47.327709Z

Source-reported events for the cited work

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Pith citing papers

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

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition cites this paper.

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

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:58:58.031350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.