Paper Citation Record · LEDGER

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks

As of 15 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2501.04816.

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

pith.paper-citation-record.v1
2501.04816 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:30:50.721191Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

26 of 26 outbound references displayed

verified exact1
verified fuzzy18
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba2ca55e-7f37-40e4-b6de-125b10bb953b · outbound

This paper cites Representing smooth functions as compositions of near-identity functions with implications for deep network optimization.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Representing smooth functions as compositions of near-identity functions with implications for deep network optimization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.613874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 909be19d-d917-4581-95f6-9024b77e63fc · outbound

This paper cites Nearest class-center sim- plification through intermediate layers.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Nearest class-center sim- plification through intermediate layers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:51.071965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30170f93-373f-4846-8df8-b12d3736c38b · outbound

This paper cites Stochastic gra- dient hamiltonian monte carlo.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Stochastic gra- dient hamiltonian monte carlo

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:51.053518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2979f62a-407b-4946-b645-4e5185b7f2db · outbound

This paper cites Laplace redux–effortless Bayesian deep learning.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Laplace redux–effortless Bayesian deep learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:51.038577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 474e4b06-c734-4eb6-92d9-de3dfb4a5436 · outbound

This paper cites Adam: A method for stochastic opti- mization.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Adam: A method for stochastic opti- mization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:51.025522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d9067888-5f69-4885-8e08-57013c9d069a · outbound

This paper cites Dropout as a bayesian ap- proximation: Representing model uncertainty in deep learn- ing.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Dropout as a bayesian ap- proximation: Representing model uncertainty in deep learn- ing

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:51.012920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8bc75766-130f-4579-90e2-f61ca178f0aa · outbound

This paper cites On the implicit bias towards minimal depth of deep neural net- works, 2022.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks On the implicit bias towards minimal depth of deep neural net- works, 2022

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.999509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b7d3b81-7917-4119-be17-59747f9aac0a · outbound

This paper cites Probabilis- tic forecasting.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Probabilis- tic forecasting

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.649600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf9f0a22-f350-4962-80eb-39da6d6c32ca · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Strictly proper scoring rules, prediction, and estimation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.654076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1df235e7-90b0-4d89-bacb-ab15880d111f · outbound

This paper cites Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:30:50.797249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7d57fa05-9da4-4744-a78a-9d5506da1edf · outbound

This paper cites Tensor decomposi- tions and applications.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Tensor decomposi- tions and applications

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.979218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c9616263-3d6d-4f82-b600-e82a291a7538 · outbound

This paper cites Simple and scalable predictive uncertainty estima- tion using deep ensembles.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Simple and scalable predictive uncertainty estima- tion using deep ensembles

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.966261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7b276da9-926d-42aa-8be4-53e583ce359c · outbound

This paper cites Gradient-based learning applied to document recog- nition.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Gradient-based learning applied to document recog- nition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.953431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7773986e-07ef-484a-9b50-f04173734fc2 · outbound

This paper cites Simple and principled uncertainty estimation with deterministic deep learning via distance awareness.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.940967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8075c19f-9749-4c26-8ea1-fd970dc2d6f2 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.676841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdad7b7e-6d30-4858-a91d-6c0d2391a8f8 · outbound

This paper cites A simple baseline for bayesian uncertainty in deep learning.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks A simple baseline for bayesian uncertainty in deep learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.680272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4437a7e-4417-4ff5-93c2-bafe662e6e5b · outbound

This paper cites Spectral Normalization for Generative Adversarial Networks.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Spectral Normalization for Generative Adversarial Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.684448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4ff630b-601f-4aad-a48c-4f0fc759d2c2 · outbound

This paper cites Deep deterministic un- certainty: A new simple baseline.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Deep deterministic un- certainty: A new simple baseline

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.921706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cf26574f-ae09-4b6d-91ed-2dd327745e08 · outbound

This paper cites Bayesian learning for neural networks , volume 118 of Lecture Notes in Statistics.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Bayesian learning for neural networks , volume 118 of Lecture Notes in Statistics

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.908968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b201e965-2cd7-4778-ba24-e0d436ebd49b · outbound

This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Prevalence of neural collapse during the terminal phase of deep learning training

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.895709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0cdd82a-1f44-4d6e-8875-08dac2dc76ec · outbound

This paper cites Feature learning in deep classifiers through intermediate neural collapse.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Feature learning in deep classifiers through intermediate neural collapse

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.882610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c951278e-6c36-46db-adbb-b053f1eb7504 · outbound

This paper cites A scalable laplace approximation for neural networks.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks A scalable laplace approximation for neural networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T21:30:50.705552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f7fe2bf-7fa0-47fb-86ac-85eaa06dea39 · outbound

This paper cites Very deep con- volutional networks for large-scale image recognition.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Very deep con- volutional networks for large-scale image recognition

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.861745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f89bd1b-1991-454d-b09f-33d3a7812a78 · outbound

This paper cites Implications of factor analysis of three- way matrices for measurement of change.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks Implications of factor analysis of three- way matrices for measurement of change

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.848277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41e3a056-716d-475d-9231-836184a7e333 · outbound

This paper cites tucker" ) # fit PSC. net = fit_psc( net=net, projection=projection, psc=.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks tucker" ) # fit PSC. net = fit_psc( net=net, projection=projection, psc=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.835235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4dab79c0-1776-4589-b35b-02fc96f7c07a · outbound

This paper cites During training, we apply the SN procedure from Mukhoti et al.

Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks During training, we apply the SN procedure from Mukhoti et al

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:30:50.822343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.