Paper Citation Record · LEDGER

laplax -- Laplace Approximations with JAX

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2507.17013.

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

pith.paper-citation-record.v1
2507.17013 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:03:56.706815Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:02:12.278733Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:36:01.402377Z

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy7
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9cd33654-9002-40d0-835c-b5eedd7be7ac · outbound

This paper cites an unresolved cited work.

laplax -- Laplace Approximations with JAX Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:03:57.029618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e2a07eaf-3c36-450e-94d5-4c24e61a9c87 · outbound

This paper cites FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning.

laplax -- Laplace Approximations with JAX FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.567195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d830f885-1a7b-47f9-a780-a1dca6c21137 · outbound

This paper cites Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood.

laplax -- Laplace Approximations with JAX Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.960700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a66727d8-10d9-4f13-9ee3-a3e2c6af349a · outbound

This paper cites Fast Predictive Uncertainty for Classification with Bayesian Deep Networks.

laplax -- Laplace Approximations with JAX Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.941670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4ff0cb7-fea7-4fc0-a808-6fc0cc15ae06 · outbound

This paper cites Learnable Uncertainty under Laplace Approximations.

laplax -- Laplace Approximations with JAX Learnable Uncertainty under Laplace Approximations

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.927753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3e9d520-7691-4606-ae1b-7622ca0eeba0 · outbound

This paper cites Mean-Field Approximation to Gaussian-Softmax Integral with Application to Uncertainty Estimation.

laplax -- Laplace Approximations with JAX Mean-Field Approximation to Gaussian-Softmax Integral with Application to Uncertainty Estimation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.663844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff90a16-4711-49bd-b389-1555c17406aa · outbound

This paper cites Linearization Turns Neural Operators into Function-Valued Gaussian Processes.

laplax -- Laplace Approximations with JAX Linearization Turns Neural Operators into Function-Valued Gaussian Processes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.666336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5530f4b-55f5-48b0-a426-e6833a6d97d1 · outbound

This paper cites Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

laplax -- Laplace Approximations with JAX Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.668930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61e6a39b-4964-4891-b5f1-6959b18771c7 · outbound

This paper cites URL https://doi.org/10.21105/joss.04455.

laplax -- Laplace Approximations with JAX URL https://doi.org/10.21105/joss.04455

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.671316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78f6ad6e-8749-45f2-b9c6-5fbc76d43a3b · outbound

This paper cites Reparameterization invariance in approximate Bayesian inference.

laplax -- Laplace Approximations with JAX Reparameterization invariance in approximate Bayesian inference

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.883840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39f49409-29a4-4b22-bdc9-d217a6fa7067 · outbound

This paper cites 6 laplax – Laplace Approximations with JAX Sun, S., Zhang, G., Shi, J., and Grosse, R.

laplax -- Laplace Approximations with JAX 6 laplax – Laplace Approximations with JAX Sun, S., Zhang, G., Shi, J., and Grosse, R

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:03:56.874931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fccc46f8-587f-44b7-9cf0-4d8b7a9385c8 · outbound

This paper cites Learning Layer-wise Equivariances Automatically using Gradients.

laplax -- Laplace Approximations with JAX Learning Layer-wise Equivariances Automatically using Gradients

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:03:56.757274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1c14f408-eaf5-488a-9681-6860f4707949 · outbound

This paper cites The LLM Surgeon.

laplax -- Laplace Approximations with JAX The LLM Surgeon

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.686638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24efffe4-41b1-457a-8829-d58659eebf35 · outbound

This paper cites (8) • MEAN FIELD 0 PREDICTIVE.

laplax -- Laplace Approximations with JAX (8) • MEAN FIELD 0 PREDICTIVE

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.073503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a6161ff6-e2a8-4fc1-b831-7db0a97d8da4 · outbound

This paper cites B Applications and extensions of the Laplace approximation Section 4 discusses Laplace approximation with the goal of calibrated predictive uncertainty.

laplax -- Laplace Approximations with JAX B Applications and extensions of the Laplace approximation Section 4 discusses Laplace approximation with the goal of calibrated predictive uncertainty

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.067966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7348ac9c-8a29-4dc8-bb45-93589fc6f924 · outbound

This paper cites (2025) lift the method to the setting of operator learning.

laplax -- Laplace Approximations with JAX (2025) lift the method to the setting of operator learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.061895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23c0c778-3215-4776-8671-ef91c0997497 · outbound

This paper cites for learning layerwise equivariance (van der Ouderaa et al., 2023).

laplax -- Laplace Approximations with JAX for learning layerwise equivariance (van der Ouderaa et al., 2023)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.055092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7724316-f62a-4656-b594-5f9294c0de07 · outbound

This paper cites Various other applications exist and this non-extensive list aimed only at provided some pointers for potential use cases.

laplax -- Laplace Approximations with JAX Various other applications exist and this non-extensive list aimed only at provided some pointers for potential use cases

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.048789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fddf0f05-025a-4b45-af82-6071a5797fc7 · outbound

This paper cites This yields a more refined MAP estimate and well-calibrated epistemic uncertainties when prior knowledge is available.

laplax -- Laplace Approximations with JAX This yields a more refined MAP estimate and well-calibrated epistemic uncertainties when prior knowledge is available

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.042556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation af524252-e39c-45c0-bb8c-18d35ec9f3ae · outbound

This paper cites This regulariser (eq.

laplax -- Laplace Approximations with JAX This regulariser (eq

Reference 1992

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:57.035804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28b1ec2d-3c97-4227-b455-c774706ab874 · outbound

This paper cites Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting.

laplax -- Laplace Approximations with JAX Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

Reference 2018

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.893235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 55936bab-aa83-475c-b62f-497b5bf6feee · outbound

This paper cites Functional Variational Bayesian Neural Networks.

laplax -- Laplace Approximations with JAX Functional Variational Bayesian Neural Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.680959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation becc4ad5-acb9-4d7e-b259-e3fd3a94c632 · outbound

This paper cites Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks.

laplax -- Laplace Approximations with JAX Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.655983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c918087a-a84a-4c77-89c1-b0c4124651e1 · outbound

This paper cites Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning.

laplax -- Laplace Approximations with JAX Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.642670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65b48e54-33f9-43ee-b701-81165ecda8a8 · outbound

This paper cites Priors in Bayesian Deep Learning: A Review.

laplax -- Laplace Approximations with JAX Priors in Bayesian Deep Learning: A Review

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.644997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4554d02b-2305-436b-bbc5-de3d53046028 · outbound

This paper cites Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization.

laplax -- Laplace Approximations with JAX Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:03:56.918963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43a26124-beda-4f7d-b387-ce7ca212c31d · outbound

This paper cites URL https://docs.

laplax -- Laplace Approximations with JAX URL https://docs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.564720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d293932-d2ee-44c1-a119-01f3db328bc0 · outbound

This paper cites Position: Curvature Matrices Should Be Democratized via Linear Operators.

laplax -- Laplace Approximations with JAX Position: Curvature Matrices Should Be Democratized via Linear Operators

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:56.569578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 729b81d3-3109-4ad8-97db-2b6cd01647be · inbound

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning cites this paper.

VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning laplax -- Laplace Approximations with JAX

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:36:01.407264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.