Paper Citation Record · LEDGER

Joint Learning of Energy-based Models and their Partition Function

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2501.18528.

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

pith.paper-citation-record.v1
2501.18528 v3

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

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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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Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation dd4472dd-e12b-4f50-9ff1-df1aa20389de · outbound

This paper cites write newline.

Joint Learning of Energy-based Models and their Partition Function write newline

Reference 1

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This paper cites H., Hinton, G.

Joint Learning of Energy-based Models and their Partition Function H., Hinton, G

Reference 2

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 3

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 4

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This paper cites Generalized Energy Based Models.

Joint Learning of Energy-based Models and their Partition Function Generalized Energy Based Models

Reference 5

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This paper cites Learning theory from first principles.

Joint Learning of Energy-based Models and their Partition Function Learning theory from first principles

Reference 6

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Joint Learning of Energy-based Models and their Partition Function The Elements of Differentiable Programming

Reference 7

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This paper cites F., and Niculae, V.

Joint Learning of Energy-based Models and their Partition Function F., and Niculae, V

Reference 8

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Joint Learning of Energy-based Models and their Partition Function Fast differentiable sorting and ranking

Reference 9

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This paper cites Learning energy networks with generalized Fenchel--young losses.

Joint Learning of Energy-based Models and their Partition Function Learning energy networks with generalized Fenchel--young losses

Reference 10

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

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This paper cites Convex optimization.

Joint Learning of Energy-based Models and their Partition Function Convex optimization

Reference 12

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Joint Learning of Energy-based Models and their Partition Function J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 13

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This paper cites On information-type measure of difference of probability distributions and indirect observations.

Joint Learning of Energy-based Models and their Partition Function On information-type measure of difference of probability distributions and indirect observations

Reference 14

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Joint Learning of Energy-based Models and their Partition Function and Igel, C

Reference 15

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This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Joint Learning of Energy-based Models and their Partition Function Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 16

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Joint Learning of Energy-based Models and their Partition Function and Hirayama, J.-i

Reference 17

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Joint Learning of Energy-based Models and their Partition Function and Hyv \"a rinen, A

Reference 18

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 19

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Joint Learning of Energy-based Models and their Partition Function and Ermon, S

Reference 20

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Joint Learning of Energy-based Models and their Partition Function Some extensions of score matching

Reference 21

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Joint Learning of Energy-based Models and their Partition Function and Dayan, P

Reference 22

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

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Joint Learning of Energy-based Models and their Partition Function and Tsitsiklis, J

Reference 24

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Joint Learning of Energy-based Models and their Partition Function M., Filippov, A., and Burnaev, E

Reference 25

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 26

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This paper cites Conditional random fields: Probabilistic models for segmenting and labeling sequence data.

Joint Learning of Energy-based Models and their Partition Function Conditional random fields: Probabilistic models for segmenting and labeling sequence data

Reference 27

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This paper cites A tutorial on energy-based learning.

Joint Learning of Energy-based Models and their Partition Function A tutorial on energy-based learning

Reference 28

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Joint Learning of Energy-based Models and their Partition Function and Astudillo, R

Reference 29

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Joint Learning of Energy-based Models and their Partition Function F., Treviso, M., Farinhas, A., Aguiar, P

Reference 30

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Joint Learning of Energy-based Models and their Partition Function Concrete score matching: Generalized score matching for discrete data

Reference 31

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Joint Learning of Energy-based Models and their Partition Function Geometric losses for distributional learning

Reference 32

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Joint Learning of Energy-based Models and their Partition Function f-GAN : Training generative neural samplers using variational divergence minimization

Reference 33

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Joint Learning of Energy-based Models and their Partition Function A family of computationally efficient and simple estimators for unnormalized statistical models

Reference 34

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Joint Learning of Energy-based Models and their Partition Function K., Roulet, V., Kakade, S

Reference 35

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Joint Learning of Energy-based Models and their Partition Function D., Ermon, S., and Finn, C

Reference 36

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Joint Learning of Energy-based Models and their Partition Function E., Puigcerver, J., Djolonga, J., Peyr \'e , G., and Blondel, M

Reference 37

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Joint Learning of Energy-based Models and their Partition Function Proximal Policy Optimization Algorithms

Reference 38

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Joint Learning of Energy-based Models and their Partition Function B., Flamary, R., Courty, N., Rolet, A., and Blondel, M

Reference 39

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Observation 81c6932d-3ed8-4b06-b84e-264225288cd2 · outbound

This paper cites Learning Energy-Based Models by Self-normalising the Likelihood.

Joint Learning of Energy-based Models and their Partition Function Learning Energy-Based Models by Self-normalising the Likelihood

Reference 40

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This paper cites How to Train Your Energy-Based Models.

Joint Learning of Energy-based Models and their Partition Function How to Train Your Energy-Based Models

Reference 41

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Joint Learning of Energy-based Models and their Partition Function and McCallum, A

Reference 42

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 43

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Observation 715b437a-1234-4e65-af91-644add7eb222 · outbound

This paper cites Learning Trans-dimensional Random Fields with Applications to Language Modeling.

Joint Learning of Energy-based Models and their Partition Function Learning Trans-dimensional Random Fields with Applications to Language Modeling

Reference 44

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Observation 35cbf327-568b-46e2-a582-58f7a7a31b4e · outbound

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Joint Learning of Energy-based Models and their Partition Function Unresolved cited work

Reference 45

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This paper cites Efficient computation of expectations under spanning tree distributions.

Joint Learning of Energy-based Models and their Partition Function Efficient computation of expectations under spanning tree distributions

Reference 46

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Pith citing papers

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