The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone | Paper Citation Record

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone ChemBERTa-2: Towards Chemical Foundation Models

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This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 4

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone H., Hearin A

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This paper cites Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect

Reference 6

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This paper cites A Conceptual Introduction to Hamiltonian Monte Carlo.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone A Conceptual Introduction to Hamiltonian Monte Carlo

Reference 8

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This paper cites The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data

Reference 9

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Pergamon Press

Reference 10

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method

Reference 11

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Stochastic Gradient Hamiltonian Monte Carlo

Reference 12

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone PaLM: Scaling Language Modeling with Pathways

Reference 14

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone N., Wild S

Reference 15

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This paper cites Automatic Zig-Zag sampling in practice.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Automatic Zig-Zag sampling in practice

Reference 16

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone NICE: Non-linear Independent Components Estimation

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

Reference 18

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone D., Pendleton B

Reference 19

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone J., Deem M

Reference 20

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This paper cites P., 2008, @doi [ ] 10.1111/j.1365-2966.2007.12353.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.384..449F 384, 449.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone P., 2008, @doi [ ] 10.1111/j.1365-2966.2007.12353.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.384..449F 384, 449

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Importance Nested Sampling and the MultiNest Algorithm

Reference 22

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This paper cites W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , http://adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , http://adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone D., 1990, @doi [Physica D: Nonlinear Phenomena] https://doi.org/10.1016/0167-2789(90)90019-L , 43, 105

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone What is the Role of Large Language Models in the Evolution of Astronomy Research?

Reference 25

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone K., 1970, @doi [Biometrika] 10.1093/biomet/57.1.97 , https://ui.adsabs.harvard.edu/abs/1970Bimka..57...97H 57, 97

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Reference 31

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Deep Residual Learning for Image Recognition

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone What Are Bayesian Neural Network Posteriors Really Like?

Reference 33

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Variational Inference with Normalizing Flows

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Adam: A Method for Stochastic Optimization

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Self-Normalizing Neural Networks

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone pp 4188--4188, @doi 10.1109/PIERS.2016.7735574

Reference 40

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Deep Ensembles Secretly Perform Empirical Bayes

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone V., 2023, @doi [Appl

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone W., Rosenbluth M

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone MCMC using Hamiltonian dynamics

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone GPT-4 Technical Report

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone O., Tweedie R

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Microcanonical Hamiltonian Monte Carlo

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

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The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone R., 2003, in Erbacher R

Reference 61

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Observation e8550e88-2d96-4863-a40a-1b60cd4c6c67 · outbound

This paper cites V., eds, American Institute of Physics Conference Series Vol.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone V., eds, American Institute of Physics Conference Series Vol

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:38.553177Z

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Observation 155e7a25-5cd6-4e16-928b-85f703fbec0c · outbound

This paper cites D., 1996.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone D., 1996

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:38.633311Z

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Unavailable: canonical work link unavailable.

Observation 51c8c702-7837-447e-95aa-a9db6d07b299 · outbound

This paper cites S., 2020, @doi [ ] 10.1093/mnras/staa278 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S 493, 3132.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone S., 2020, @doi [ ] 10.1093/mnras/staa278 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S 493, 3132

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:38.686052Z

Source-reported events for the cited work

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Observation 2c8898ef-8e27-40cd-97a9-69e054c496fd · outbound

This paper cites Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

Reference 65

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Source-reported events for the cited work

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Observation 7fdbbb2f-d3ff-4518-8263-d11eaa4072a1 · outbound

This paper cites G., Vanden-Eijnden E., 2010, Communications in Mathematical Sciences, 8, 217.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone G., Vanden-Eijnden E., 2010, Communications in Mathematical Sciences, 8, 217

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:38.844035Z

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Unavailable: canonical work link unavailable.

Observation b2119e87-4e59-4901-aeef-7f6e65d56743 · outbound

This paper cites Teaching Astronomy with Large Language Models.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Teaching Astronomy with Large Language Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:38.924028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78367120-7ae2-497f-b456-4049e3a64372 · outbound

This paper cites K., 2001, Scandinavian Journal of Statistics, 28, 205.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone K., 2001, Scandinavian Journal of Statistics, 28, 205

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.002183Z

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Unavailable: canonical work link unavailable.

Observation 15eece01-3a37-4612-8b48-c7d795605a57 · outbound

This paper cites Attention Is All You Need.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Attention Is All You Need

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.021971Z

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Observation 5819ccf5-21a7-4341-84c0-b468ba36ee20 · outbound

This paper cites an unresolved cited work.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Unresolved cited work

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.123617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc2b3aa-d292-49ac-bffb-091d2e1f37cf · outbound

This paper cites How Good is the Bayes Posterior in Deep Neural Networks Really?.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone How Good is the Bayes Posterior in Deep Neural Networks Really?

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.215715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124106d8-1f3e-49e6-a50a-f7e521b85860 · outbound

This paper cites an unresolved cited work.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Unresolved cited work

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.323919Z

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Unavailable: canonical work link unavailable.

Observation ba195b92-2f13-4714-bfab-971263a3340d · outbound

This paper cites F., Boada S., 2019, @doi [ ] 10.1093/mnras/stz333 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4683W 484, 4683.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone F., Boada S., 2019, @doi [ ] 10.1093/mnras/stz333 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.4683W 484, 4683

Reference 73

Resolution: verified exact; doi, observed 2026-08-04T07:38:32.383767Z

Source-reported events for the cited work

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Observation 01f4bea0-d607-422a-b256-8d89916970c2 · outbound

This paper cites an unresolved cited work.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Unresolved cited work

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.400737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaad0e4a-c3c1-4ef5-bef1-a0c9ac1ed51b · outbound

This paper cites I., 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone I., 2019, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.534832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 098ce06e-6dda-474d-b7c6-9b0697f343b5 · outbound

This paper cites T., Wang W., Bai J., Wang Z., Song Y., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2505.13259 , https://ui.adsabs.harvard.edu/abs/2025arXiv250513259Z p.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone T., Wang W., Bai J., Wang Z., Song Y., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2505.13259 , https://ui.adsabs.harvard.edu/abs/2025arXiv250513259Z p

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.618059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 256e36f3-42a4-4cc6-b44c-b0ea6496baa0 · outbound

This paper cites an unresolved cited work.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.676462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63bfb5a9-caa4-459e-bd8a-791427e81d2d · outbound

This paper cites write newline.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone write newline

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-04T07:36:39.751274Z

Source-reported events for the cited work

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Coverage vector

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Outbound references

Pith citing papers