Paper Citation Record · LEDGER

Deep Generative Models with Hard Linear Equality Constraints

As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2502.05416.

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

pith.paper-citation-record.v1
2502.05416 v2

Coverage vector

measured 54 of 54 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 6d3b5469-b573-45d8-a168-a8bacc988fc3 · outbound

This paper cites write newline.

Deep Generative Models with Hard Linear Equality Constraints write newline

Reference 1

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Observation f91ae176-824b-49b6-acb6-0eded2e9d6ab · outbound

This paper cites Semantic probabilistic layers for neuro-symbolic learning.

Deep Generative Models with Hard Linear Equality Constraints Semantic probabilistic layers for neuro-symbolic learning

Reference 2

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Observation 83f88985-b6f3-4fb5-ba23-3b9293b303cd · outbound

This paper cites Simple: A gradient estimator for k-subset sampling.

Deep Generative Models with Hard Linear Equality Constraints Simple: A gradient estimator for k-subset sampling

Reference 3

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Observation 97d8a276-50ef-4c4c-ba42-79033df161f5 · outbound

This paper cites and Kolter, J.

Deep Generative Models with Hard Linear Equality Constraints and Kolter, J

Reference 4

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Observation c96a2090-9c19-40ec-8fc3-8bb52d536ebc · outbound

This paper cites d., Serafini, L., and Spranger, M.

Deep Generative Models with Hard Linear Equality Constraints d., Serafini, L., and Spranger, M

Reference 5

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Observation c57b3f2a-1aec-43f8-9a4d-1d7a3d0367c9 · outbound

This paper cites Importance weighted autoencoders.

Deep Generative Models with Hard Linear Equality Constraints Importance weighted autoencoders

Reference 6

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Observation 03c7d3d2-4c78-4f91-b54e-57102ed84c6a · outbound

This paper cites and Kwon, R.

Deep Generative Models with Hard Linear Equality Constraints and Kwon, R

Reference 7

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Observation 3222a691-60ee-41ef-b8fa-dfc711750979 · outbound

This paper cites Spectral temporal graph neural network for multivariate time-series forecasting.

Deep Generative Models with Hard Linear Equality Constraints Spectral temporal graph neural network for multivariate time-series forecasting

Reference 8

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 9

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Observation d860e8f1-9bc3-4730-8242-72d566d328c0 · outbound

This paper cites Learning to explain: An information-theoretic perspective on model interpretation.

Deep Generative Models with Hard Linear Equality Constraints Learning to explain: An information-theoretic perspective on model interpretation

Reference 10

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Observation bb7101c4-a393-4e1c-bcf5-2eac481ce56d · outbound

This paper cites Problog: A probabilistic prolog and its application in link discovery.

Deep Generative Models with Hard Linear Equality Constraints Problog: A probabilistic prolog and its application in link discovery

Reference 11

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Observation 3027fc9b-7284-4653-8b04-aa4c521b0f1d · outbound

This paper cites Efficient generation of structured objects with constrained adversarial networks.

Deep Generative Models with Hard Linear Equality Constraints Efficient generation of structured objects with constrained adversarial networks

Reference 12

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Observation 7ee4efd5-3e09-48df-82ac-afa8cb6194cb · outbound

This paper cites and Zabell, S.

Deep Generative Models with Hard Linear Equality Constraints and Zabell, S

Reference 13

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Observation 238b7253-386b-4b8d-bae6-910fc195d7ec · outbound

This paper cites Bridging logic and kernel machines.

Deep Generative Models with Hard Linear Equality Constraints Bridging logic and kernel machines

Reference 14

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Observation 81b6e334-b4bd-48c8-b057-c2e8acbacf8c · outbound

This paper cites and Krause, A.

Deep Generative Models with Hard Linear Equality Constraints and Krause, A

Reference 15

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 16

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Observation 46f52ed0-4538-49d9-89f2-b33ec3cd6d7d · outbound

This paper cites Dl2: training and querying neural networks with logic.

Deep Generative Models with Hard Linear Equality Constraints Dl2: training and querying neural networks with logic

Reference 17

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Observation a5ddf314-7bb7-4353-9717-94bfb0136b68 · outbound

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 18

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Observation a4dee12c-dd8a-44f4-b084-a19921cfce8d · outbound

This paper cites and Lukasiewicz, T.

Deep Generative Models with Hard Linear Equality Constraints and Lukasiewicz, T

Reference 19

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Observation 25275d79-9c46-4def-b674-5d994824fc0a · outbound

This paper cites Stochastic optimization of sorting networks via continuous relaxations.

Deep Generative Models with Hard Linear Equality Constraints Stochastic optimization of sorting networks via continuous relaxations

Reference 20

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Observation 9ebd50d3-9fc1-4391-b8e6-23d45fc3683c · outbound

This paper cites Variational autoencoders with jointly optimized latent dependency structure.

Deep Generative Models with Hard Linear Equality Constraints Variational autoencoders with jointly optimized latent dependency structure

Reference 21

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Observation 365e805a-44af-40f2-b742-d5e8244426de · outbound

This paper cites Linearly Constrained Neural Networks.

Deep Generative Models with Hard Linear Equality Constraints Linearly Constrained Neural Networks

Reference 22

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Observation 660fac57-1107-4fa9-8769-ebff5f840128 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Deep Generative Models with Hard Linear Equality Constraints Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 23

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Observation f144a813-7076-4986-a79e-937477986947 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Deep Generative Models with Hard Linear Equality Constraints Denoising Diffusion Probabilistic Models

Reference 24

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Observation d8dd7d56-08c5-4ec3-95de-a86dd1c5eb79 · outbound

This paper cites Categorical reparameterization with gumbel-softmax.

Deep Generative Models with Hard Linear Equality Constraints Categorical reparameterization with gumbel-softmax

Reference 25

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Observation 72a8f06e-7118-45cf-ab73-c76cfe8b49e9 · outbound

This paper cites Exact sampling with integer linear programs and random perturbations.

Deep Generative Models with Hard Linear Equality Constraints Exact sampling with integer linear programs and random perturbations

Reference 26

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Observation 2fe5c329-351a-41a6-8c74-4f9421607473 · outbound

This paper cites Auto-Encoding Variational Bayes.

Deep Generative Models with Hard Linear Equality Constraints Auto-Encoding Variational Bayes

Reference 27

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

Deep Generative Models with Hard Linear Equality Constraints Learning multiple layers of features from tiny images

Reference 28

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Observation b9172846-0528-4e65-bca5-106ffadf0c16 · outbound

This paper cites Efficient dependency models: Simulating dependent random variables.

Deep Generative Models with Hard Linear Equality Constraints Efficient dependency models: Simulating dependent random variables

Reference 29

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 30

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Observation 849b567f-cf63-4663-95eb-a321100179b7 · outbound

This paper cites Deep learning face attributes in the wild.

Deep Generative Models with Hard Linear Equality Constraints Deep learning face attributes in the wild

Reference 31

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This paper cites DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps.

Deep Generative Models with Hard Linear Equality Constraints DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Reference 32

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Deep Generative Models with Hard Linear Equality Constraints J., Mnih, A., and Teh, Y

Reference 33

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This paper cites Vael: Bridging variational autoencoders and probabilistic logic programming.

Deep Generative Models with Hard Linear Equality Constraints Vael: Bridging variational autoencoders and probabilistic logic programming

Reference 34

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 35

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Observation 4862d70f-f60c-4de3-b39f-1f2eb5dd4cb6 · outbound

This paper cites M., and Fern, X.

Deep Generative Models with Hard Linear Equality Constraints M., and Fern, X

Reference 36

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Observation f2aefa11-3b74-4abe-830f-edd1464dd63d · outbound

This paper cites Improved techniques for training gans.

Deep Generative Models with Hard Linear Equality Constraints Improved techniques for training gans

Reference 37

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 38

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This paper cites A unified approach to count-based weakly supervised learning.

Deep Generative Models with Hard Linear Equality Constraints A unified approach to count-based weakly supervised learning

Reference 39

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This paper cites K., Raiko, T., Maal e, L., S nderby, S.

Deep Generative Models with Hard Linear Equality Constraints K., Raiko, T., Maal e, L., S nderby, S

Reference 40

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Observation ce76b5f3-1b23-4fb6-aa17-aa1f8bdab576 · outbound

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Deep Generative Models with Hard Linear Equality Constraints Denoising diffusion implicit models

Reference 41

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Deep Generative Models with Hard Linear Equality Constraints and Ermon, S

Reference 42

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Observation ba9ffa87-5296-4005-91c1-26bdc2f06379 · outbound

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Deep Generative Models with Hard Linear Equality Constraints C., Giunchiglia, E., and Lukasiewicz, T

Reference 43

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Observation 42de9832-1de4-48d6-8a9d-8e3e6eaecaf1 · outbound

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Deep Generative Models with Hard Linear Equality Constraints C., Dyrmishi, S., Cordy, M., Lukasiewicz, T., and Giunchiglia, E

Reference 44

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Observation 5f705e6f-fdae-4acb-bbe9-9f46e8848881 · outbound

This paper cites Differentiable submodular maximization.

Deep Generative Models with Hard Linear Equality Constraints Differentiable submodular maximization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.147997Z

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Observation 68f7b268-009d-40a6-93e5-7d4fc7516d4c · outbound

This paper cites Sampling the multivariate standard normal distribution under a weighted sum constraint.

Deep Generative Models with Hard Linear Equality Constraints Sampling the multivariate standard normal distribution under a weighted sum constraint

Reference 46

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Observation a3613ef6-0d5d-4653-a5a6-78810907ffe9 · outbound

This paper cites LinSATNet : The positive linear satisfiability neural networks.

Deep Generative Models with Hard Linear Equality Constraints LinSATNet : The positive linear satisfiability neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.130201Z

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Observation f2841657-01e1-4e81-b892-09590ddb58ac · outbound

This paper cites Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization.

Deep Generative Models with Hard Linear Equality Constraints Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.112497Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f7121052-b589-4753-8dcb-ba842bcf3f3a · outbound

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Deep Generative Models with Hard Linear Equality Constraints Unresolved cited work

Reference 49

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Observation ea3fdac3-2e7c-4b1c-a206-d86d7f227812 · outbound

This paper cites A semantic loss function for deep learning with symbolic knowledge.

Deep Generative Models with Hard Linear Equality Constraints A semantic loss function for deep learning with symbolic knowledge

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.077516Z

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Observation abc08d50-c6c4-4dee-91cc-344e3dabb91e · outbound

This paper cites Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop.

Deep Generative Models with Hard Linear Equality Constraints Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.060795Z

Source-reported events for the cited work

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Observation beb5a93b-c467-481c-b599-5974b43bd9fe · outbound

This paper cites Physdiff: Physics-guided human motion diffusion model.

Deep Generative Models with Hard Linear Equality Constraints Physdiff: Physics-guided human motion diffusion model

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.037636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 789612e0-7e8b-4aa4-89a6-1d0063783888 · outbound

This paper cites H., Meng, T., Chang, K.-W., and Van den Broeck, G.

Deep Generative Models with Hard Linear Equality Constraints H., Meng, T., Chang, K.-W., and Van den Broeck, G

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.019489Z

Source-reported events for the cited work

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Observation 7054563a-bd03-4ed2-862b-e1f09545a26b · outbound

This paper cites Deep learning for portfolio optimization.

Deep Generative Models with Hard Linear Equality Constraints Deep learning for portfolio optimization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:33:57.001504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.