Paper Citation Record · LEDGER

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces

As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2412.08515.

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

pith.paper-citation-record.v1
2412.08515 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:48:18.452480Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation d53fa95d-4525-4c15-89cb-f24fff59b349 · outbound

This paper cites & Bawany, N.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Bawany, N

Reference 1

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Kaiserslautern-Landau, R

Reference 2

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This paper cites Towards certifiable AI in aviation: landscape, challenges, and opportunities.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Towards certifiable AI in aviation: landscape, challenges, and opportunities

Reference 3

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Making machine learning trustworthy

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Unresolved cited work

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & van der Schaar, M

Reference 6

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Observation 8c558839-31db-4cc1-9684-a9aa0e4e8d1e · outbound

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Ommer, B

Reference 7

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Unresolved cited work

Reference 8

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Reference 9

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This paper cites Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models

Reference 10

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Observation 71aaebe5-d39b-43eb-8ed8-13b6d56bdc35 · outbound

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Song, X

Reference 11

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Reference 12

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Bilge, H

Reference 13

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Hart, P

Reference 14

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This paper cites Support-vector networks.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Support-vector networks

Reference 15

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Observation 19de8ed2-e414-4fbc-b00e-38aa0dd2dc32 · outbound

This paper cites & Sun, J.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Sun, J

Reference 16

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Observation c5dc4676-84c4-4adc-b7cc-565bdf27d4c9 · outbound

This paper cites K., Waiyamai, K., Konstantinos, S.

Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces K., Waiyamai, K., Konstantinos, S

Reference 17

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Reference 18

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Lukowicz, P

Reference 19

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces A boosting approach for supervised mahalanobis distance metric learning

Reference 20

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces V ., Kulis, B., Jain, P., Sra, S

Reference 22

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & LeCun, Y

Reference 23

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Chen, K

Reference 24

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Shen, H

Reference 25

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Hinton, G

Reference 27

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Philbin, J

Reference 29

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Leibe, B

Reference 30

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Singh, S

Reference 31

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Metric Learning with Adaptive Density Discrimination

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Zafeiriou, S

Reference 34

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Basu, S

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Douze, M

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & LeCun, Y

Reference 42

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Improved deep metric learning with multi-class n-pair loss objective

Reference 43

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 46

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Sun, J

Reference 47

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces Adam: A Method for Stochastic Optimization

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Hinton, G

Reference 50

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Reference 51

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces M., Aliguliyev, R

Reference 52

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Luma, A

Reference 53

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Enhancing Interpretability Through Loss-Defined Classification Objective in Structured Latent Spaces & Shao, J

Reference 54

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Pith citing papers

No inbound Pith citation observations are available.