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Paper Citation Record · LEDGER

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.01191.

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

pith.paper-citation-record.v1
2502.01191 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:21:13.551023Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

28 of 28 outbound references displayed

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  • verified fuzzy6
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External citation measurements

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Outbound references

Observation 11b5d12e-d8c2-42f3-a980-b0e36e7b0715 · outbound

This paper cites write newline.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model write newline

Reference 1

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Observation 0a20e466-b601-4b9b-ac74-f3e5ceba5d94 · outbound

This paper cites Representation learning: A review and new perspectives.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Representation learning: A review and new perspectives

Reference 2

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Observation dbd2bf00-03ae-44ec-8e68-d8d48a47a707 · outbound

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

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 3

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Observation 3c665d4e-fbb9-48a1-833c-129215be26ca · outbound

This paper cites C., and Wen, B.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model C., and Wen, B

Reference 4

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Observation c175aeb9-65ce-4d27-b100-ec3229ed76db · outbound

This paper cites Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation

Reference 5

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verified exact
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Source-reported events for the cited work

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Observation 86d9325f-768f-4485-bdbc-ce95f0d397f0 · outbound

This paper cites Measuring statistical dependence with hilbert-schmidt norms.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Measuring statistical dependence with hilbert-schmidt norms

Reference 6

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Observation dc9fa9e5-f7ff-44f6-8a4b-e7b347f0289a · outbound

This paper cites Addressing leakage in concept bottleneck models.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Addressing leakage in concept bottleneck models

Reference 7

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Observation 78634149-b1e9-4587-b36d-1b4b31568638 · outbound

This paper cites Deep residual learning for image recognition.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Deep residual learning for image recognition

Reference 8

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Observation d99982f0-b208-4ca5-a30c-e0271ba55770 · outbound

This paper cites Towards a Definition of Disentangled Representations.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Towards a Definition of Disentangled Representations

Reference 9

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Observation c3ab92ac-7d5c-49bd-826d-ad254ac94180 · outbound

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

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Ib-gan: Disentangled representation learning with information bottleneck generative adversarial networks

Reference 10

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Observation 01f12709-c528-4f43-a40c-226593ef5420 · outbound

This paper cites Probabilistic Concept Bottleneck Models.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Probabilistic Concept Bottleneck Models

Reference 11

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Observation 33da37b2-2845-4677-926a-444917d1bbf6 · outbound

This paper cites W., Nguyen, T., Tang, Y.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model W., Nguyen, T., Tang, Y

Reference 12

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Observation b468bf73-a093-48f9-8d6b-b4f841d98b79 · outbound

This paper cites Oogan: Disentangling gan with one-hot sampling and orthogonal regularization.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Oogan: Disentangling gan with one-hot sampling and orthogonal regularization

Reference 13

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Source-reported events for the cited work

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Observation ab001dc3-972a-465c-8d40-c75a6f254de6 · outbound

This paper cites Activity image-to-video retrieval by disentangling appearance and motion.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Activity image-to-video retrieval by disentangling appearance and motion

Reference 14

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Observation e3a53910-e88a-480e-9b71-82bc2d86c2d7 · outbound

This paper cites Deep learning face attributes in the wild.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Deep learning face attributes in the wild

Reference 15

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Observation 959d1b59-4b21-4332-a6c2-48abd5d0615f · outbound

This paper cites Promises and Pitfalls of Black-Box Concept Learning Models.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Promises and Pitfalls of Black-Box Concept Learning Models

Reference 16

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Observation 3fe456a0-37f5-43d7-ab89-776693cf5414 · outbound

This paper cites Do Concept Bottleneck Models Learn as Intended?.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Do Concept Bottleneck Models Learn as Intended?

Reference 17

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Observation c1c4c6ed-d953-4149-a876-ea27a2f58f64 · outbound

This paper cites and Ebrahimi Kahou, S.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model and Ebrahimi Kahou, S

Reference 18

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Source-reported events for the cited work

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Observation f0195e88-3f8c-4abe-897b-3cb2e35080c3 · outbound

This paper cites Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness

Reference 19

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Observation dbf1f430-fba0-42ce-ae6b-9295e579b6e5 · outbound

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Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Disentangled representation learning gan for pose-invariant face recognition

Reference 20

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This paper cites Disentangling instructive information from ranked multiple candidates for multi-document scientific summarization.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Disentangling instructive information from ranked multiple candidates for multi-document scientific summarization

Reference 21

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verified fuzzy
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Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Caltech-ucsd birds 200

Reference 22

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Observation fe4be553-b852-4718-a853-b635927d285d · outbound

This paper cites Improving Robustness and Generality of NLP Models Using Disentangled Representations.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Improving Robustness and Generality of NLP Models Using Disentangled Representations

Reference 23

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Observation 0a6e0cc6-c5af-4fd6-b098-f55df02103d8 · outbound

This paper cites H., Schiele, B., and Akata, Z.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model H., Schiele, B., and Akata, Z

Reference 24

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Observation 5bedec56-d96c-4936-8ab1-295bf7099175 · outbound

This paper cites Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Multi-vae: Learning disentangled view-common and view-peculiar visual representations for multi-view clustering

Reference 25

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e069c331-4b33-4d62-86de-1ce43445a11d · outbound

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Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Post-hoc Concept Bottleneck Models

Reference 26

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Observation 0f0ac22e-3b91-42cf-8460-7867a4b9f2a8 · outbound

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Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model E., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Precioso, F., Melacci, S., Weller, A., Lio, P., et al

Reference 27

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Observation c2684e8e-4a71-4358-8abf-adaf63cadcdf · outbound

This paper cites Places: A 10 million image database for scene recognition.

Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model Places: A 10 million image database for scene recognition

Reference 28

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Pith citing papers

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