Paper Citation Record · LEDGER

Bridge the Inference Gaps of Neural Processes via Expectation Maximization

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:2501.03264.

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

pith.paper-citation-record.v1
2501.03264 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:21:11.332334Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:17:51.621459Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T16:10:09.201963Z

Reference resolution

27 of 27 outbound references displayed

verified exact3
verified fuzzy12
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 77bde9d6-18dd-4679-a729-87347c5ce11d · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.621823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 31f0712b-5f25-481b-8f00-f3fc996ea378 · outbound

This paper cites Let z ∈ Rd be the latent variable for a diagonal Gaussian conditional prior p(z|DC τ ; ϑ) = N (z; µϑ(DC τ ), Σϑ(DC τ )).

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Let z ∈ Rd be the latent variable for a diagonal Gaussian conditional prior p(z|DC τ ; ϑ) = N (z; µϑ(DC τ ), Σϑ(DC τ ))

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.577912Z

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Observation 010a4b40-92ca-4a6c-9acb-885c86fa41cf · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 4

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Observation 61de20ad-e74d-497e-aa4c-1f8da5e22385 · outbound

This paper cites Auto-Encoding Variational Bayes.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Auto-Encoding Variational Bayes

Reference 6

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Observation 87114b25-946b-4d86-8bec-4cb233ca8225 · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.672686Z

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Observation 00cb6a24-27fe-4591-87f7-fb08c92d74ee · outbound

This paper cites 3 3.2 Evaluation Criteria & Asymptotic Performance.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization 3 3.2 Evaluation Criteria & Asymptotic Performance

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.661896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b99f0446-966e-4d28-8014-297a50695e76 · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.641101Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7599d21-29da-43d9-9260-8284a66de0b9 · outbound

This paper cites Prior Distribution.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Prior Distribution

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.631439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f625e7c-7d88-47dc-ba48-2a1d05f754f3 · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.612129Z

Source-reported events for the cited work

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Observation 7a454eda-752d-4376-ae9b-8d1f4f06752a · outbound

This paper cites Here we denote the approximate inference gap byDAI KL and the posterior approximation gap by DPA KL in Table (5).

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Here we denote the approximate inference gap byDAI KL and the posterior approximation gap by DPA KL in Table (5)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.589244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e51d65d5-5d14-4f2a-aeaf-972740143f09 · outbound

This paper cites To enable researchers to implement our developed method in studies, we leave the anonymous Github link here: https://anonymous.4open.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization To enable researchers to implement our developed method in studies, we leave the anonymous Github link here: https://anonymous.4open

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.566997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ffc11da-f596-44dd-9410-6bbb596f0c80 · outbound

This paper cites G.2 N EURAL ARCHITECTURES & OPTIMIZATIONS & E VALUATION SET-UP Synthetic Regression.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization G.2 N EURAL ARCHITECTURES & OPTIMIZATIONS & E VALUATION SET-UP Synthetic Regression

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.543425Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 590809bf-9571-4f93-9edd-e139456524a9 · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.519697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3ac92f89-e496-445d-ba0c-cbc4085eab6d · outbound

This paper cites Our developed SI-NPs can be viewed as the conditional version of importance weighted autoen- coders, which explains the empirical observations in Fig.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Our developed SI-NPs can be viewed as the conditional version of importance weighted autoen- coders, which explains the empirical observations in Fig

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.508475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9d1238d7-4d9e-4b3f-a7bc-08d650f815d5 · outbound

This paper cites To enable fair comparison, we also augment other baselines with attention networks.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization To enable fair comparison, we also augment other baselines with attention networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.495803Z

Source-reported events for the cited work

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Observation 327160f7-325a-4b9f-9d62-e25a80e9cf70 · outbound

This paper cites We notice that the SI-ANP significantly beats other models in FMNIST/SVHN/CIFAR10 and is comparable with the ML-ANP in MNIST.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization We notice that the SI-ANP significantly beats other models in FMNIST/SVHN/CIFAR10 and is comparable with the ML-ANP in MNIST

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.483918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 10fb7dbd-7451-4231-b8d5-0a11d280f34b · outbound

This paper cites For each run, we randomly sample 1000 functions as tasks to evaluate.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization For each run, we randomly sample 1000 functions as tasks to evaluate

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.472606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0919fd3b-349e-426e-b7e6-c8e963853d15 · outbound

This paper cites Reweighted Wake-Sleep.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Reweighted Wake-Sleep

Reference 2006

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Source-reported events for the cited work

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Observation 0c3978e4-74bd-47e6-ad69-14efd7ba2d72 · outbound

This paper cites an unresolved cited work.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Unresolved cited work

Reference 2009

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:21:11.555174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 95530254-52bc-42cf-94c3-06a0bceef7de · outbound

This paper cites Reweighted Expectation Maximization.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Reweighted Expectation Maximization

Reference 2013

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Source-reported events for the cited work

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Observation bf79d17a-e353-47a7-8aaf-8e32b22f7147 · outbound

This paper cites Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T22:21:11.249383Z

Source-reported events for the cited work

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Observation c2b68634-da62-4153-a9b2-4117cb51de46 · outbound

This paper cites Uncertainty in Neural Processes.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Uncertainty in Neural Processes

Reference 2016

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:21:11.382356Z

Source-reported events for the cited work

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Observation c24e3a49-7e14-42e2-ad08-1988ae658f57 · outbound

This paper cites In other words, the consistent regularizer in NPs is ill-posed for optimization.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization In other words, the consistent regularizer in NPs is ill-posed for optimization

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.601190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 65d37e6a-243e-4479-ae62-368c7285b5b4 · outbound

This paper cites Since the meta learning exper- iment is computationally expensive and time-consuming in training processes, we do not examine combinations with other inductive biases in this paper.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Since the meta learning exper- iment is computationally expensive and time-consuming in training processes, we do not examine combinations with other inductive biases in this paper

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:11.650914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 24600a56-2803-4ab5-8c39-6b67cf45bb68 · outbound

This paper cites Meta-Learning surrogate models for sequential decision making.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Meta-Learning surrogate models for sequential decision making

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:21:11.435014Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 795110b5-b90c-42a0-92bd-19751bf0c9b7 · outbound

This paper cites Neural Processes.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Neural Processes

Reference 2021

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Source-reported events for the cited work

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Observation 7924397e-18fe-4aec-8df7-13e4f305757b · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Bridge the Inference Gaps of Neural Processes via Expectation Maximization Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:21:11.264826Z

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Pith citing papers

Observation 9dbacbf8-dc42-4c5a-b0c2-a17da4f59803 · inbound

Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes cites this paper.

Probabilistic Interactive 3D Segmentation with Hierarchical Neural Processes Bridge the Inference Gaps of Neural Processes via Expectation Maximization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T04:17:51.621459Z

Source-reported events for the cited work

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Observation 0021a57a-9aed-4be0-9904-2fa6b3b4dc72 · inbound

Distance-informed Neural Processes cites this paper.

Distance-informed Neural Processes Bridge the Inference Gaps of Neural Processes via Expectation Maximization

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:10:09.208359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.