On Equivariant Model Selection through the Lens of Uncertainty | Paper Citation Record

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This paper cites Conformal prediction: A gentle introduction.

On Equivariant Model Selection through the Lens of Uncertainty Conformal prediction: A gentle introduction

Reference 1

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This paper cites Geometric deep learning on molecular representations.

On Equivariant Model Selection through the Lens of Uncertainty Geometric deep learning on molecular representations

Reference 2

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Observation e1fc98f6-0a00-48c4-86ce-186b68844c3e · outbound

This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

On Equivariant Model Selection through the Lens of Uncertainty E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 3

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Observation 0a7d6baa-f230-4be8-a4ef-ffaab6f31818 · outbound

This paper cites The need for uncertainty quantification in machine-assisted medical decision making.

On Equivariant Model Selection through the Lens of Uncertainty The need for uncertainty quantification in machine-assisted medical decision making

Reference 4

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This paper cites Fast, expressive se(n)-equivariant networks through weight-sharing in position-orientation space.

On Equivariant Model Selection through the Lens of Uncertainty Fast, expressive se(n)-equivariant networks through weight-sharing in position-orientation space

Reference 5

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This paper cites Pattern recognition and machine learning.

On Equivariant Model Selection through the Lens of Uncertainty Pattern recognition and machine learning

Reference 6

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This paper cites Probabilistic symmetries and invariant neural networks.

On Equivariant Model Selection through the Lens of Uncertainty Probabilistic symmetries and invariant neural networks

Reference 7

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This paper cites Geometric and physical quantities improve e (3) equivariant message passing.

On Equivariant Model Selection through the Lens of Uncertainty Geometric and physical quantities improve e (3) equivariant message passing

Reference 8

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Observation 9a786840-72b0-44b0-99aa-06b244a00b87 · outbound

This paper cites Verification of forecasts expressed in terms of probability.

On Equivariant Model Selection through the Lens of Uncertainty Verification of forecasts expressed in terms of probability

Reference 9

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Observation 86867a87-622e-498c-83dc-8c1983316667 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

On Equivariant Model Selection through the Lens of Uncertainty Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 10

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Observation b04f688d-be33-41c2-9567-125faf343bfe · outbound

This paper cites Uncertainty quantification with graph neural networks for efficient molecular design.

On Equivariant Model Selection through the Lens of Uncertainty Uncertainty quantification with graph neural networks for efficient molecular design

Reference 11

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Observation cee19f78-2f01-417f-9c96-fda974a82663 · outbound

This paper cites Group equivariant convolutional networks.

On Equivariant Model Selection through the Lens of Uncertainty Group equivariant convolutional networks

Reference 12

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This paper cites Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds.

On Equivariant Model Selection through the Lens of Uncertainty Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds

Reference 13

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Observation 0150cff1-029a-4aff-bf73-70065393ec77 · outbound

This paper cites Laplace redux-effortless bayesian deep learning.

On Equivariant Model Selection through the Lens of Uncertainty Laplace redux-effortless bayesian deep learning

Reference 14

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Observation 4e4d3579-612c-4e93-bf0a-94cef3a807d5 · outbound

This paper cites A large-scale study of probabilistic calibration in neural network regression.

On Equivariant Model Selection through the Lens of Uncertainty A large-scale study of probabilistic calibration in neural network regression

Reference 15

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This paper cites Residual pathway priors for soft equivariance constraints.

On Equivariant Model Selection through the Lens of Uncertainty Residual pathway priors for soft equivariance constraints

Reference 16

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Observation 9d4d65ff-3702-49af-aa24-21dc0279cc1a · outbound

This paper cites Conformal prediction: A unified review of theory and new challenges.

On Equivariant Model Selection through the Lens of Uncertainty Conformal prediction: A unified review of theory and new challenges

Reference 17

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Observation 2bc133b1-575e-4402-a0cb-1decd941af26 · outbound

This paper cites Guest editorial special issue on geometric deep learning in medical imaging.

On Equivariant Model Selection through the Lens of Uncertainty Guest editorial special issue on geometric deep learning in medical imaging

Reference 18

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This paper cites A survey of uncertainty in deep neural networks.

On Equivariant Model Selection through the Lens of Uncertainty A survey of uncertainty in deep neural networks

Reference 19

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This paper cites Pac-bayesian theory meets bayesian inference.

On Equivariant Model Selection through the Lens of Uncertainty Pac-bayesian theory meets bayesian inference

Reference 20

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Observation 334d555d-a2c0-474f-b8be-a7a1bc4e8b6f · outbound

This paper cites Probabilistic forecasts, calibration and sharpness.

On Equivariant Model Selection through the Lens of Uncertainty Probabilistic forecasts, calibration and sharpness

Reference 21

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Observation ea4a6510-e10a-49b3-9a40-4d36d5135d77 · outbound

This paper cites On calibration of modern neural networks.

On Equivariant Model Selection through the Lens of Uncertainty On calibration of modern neural networks

Reference 22

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Observation 79e5e97e-ec76-4a7c-bd11-ee942a0db3fa · outbound

This paper cites Scalable marginal likelihood estimation for model selection in deep learning.

On Equivariant Model Selection through the Lens of Uncertainty Scalable marginal likelihood estimation for model selection in deep learning

Reference 23

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Observation 55753c69-1b9c-4cff-8308-82d36465e0cc · outbound

This paper cites Invariance learning in deep neural networks with differentiable laplace approximations.

On Equivariant Model Selection through the Lens of Uncertainty Invariance learning in deep neural networks with differentiable laplace approximations

Reference 24

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Observation 7c1d2329-fb49-48a1-bf2f-3a07ebd5a076 · outbound

This paper cites i DECOD e: In-distribution equivariance for conformal out-of-distribution detection.

On Equivariant Model Selection through the Lens of Uncertainty i DECOD e: In-distribution equivariance for conformal out-of-distribution detection

Reference 25

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Observation e5ddef7b-1ddd-42c6-b698-e531180196f9 · outbound

This paper cites Regularizing towards soft equivariance under mixed symmetries.

On Equivariant Model Selection through the Lens of Uncertainty Regularizing towards soft equivariance under mixed symmetries

Reference 26

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Observation e5f600b0-cdf1-42a2-8cf5-1e49b8d2ea63 · outbound

This paper cites Learning probabilistic symmetrization for architecture agnostic equivariance.

On Equivariant Model Selection through the Lens of Uncertainty Learning probabilistic symmetrization for architecture agnostic equivariance

Reference 27

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Observation abebdb5f-8cd0-484c-9bc0-951a68285762 · outbound

This paper cites Empirical frequentist coverage of deep learning uncertainty quantification procedures.

On Equivariant Model Selection through the Lens of Uncertainty Empirical frequentist coverage of deep learning uncertainty quantification procedures

Reference 28

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Observation f07f8b75-1843-4c94-8860-ea9d673b9b34 · outbound

This paper cites Accurate uncertainties for deep learning using calibrated regression.

On Equivariant Model Selection through the Lens of Uncertainty Accurate uncertainties for deep learning using calibrated regression

Reference 29

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Observation b2cddae1-4917-4fb1-9d6f-4d3d34b4ad34 · outbound

This paper cites Bayesian graph neural networks for molecular property prediction.

On Equivariant Model Selection through the Lens of Uncertainty Bayesian graph neural networks for molecular property prediction

Reference 30

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This paper cites Distribution-free predictive inference for regression.

On Equivariant Model Selection through the Lens of Uncertainty Distribution-free predictive inference for regression

Reference 31

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Observation fbeb8b84-dea8-4c1f-8463-a3b32e60fcca · outbound

This paper cites Marginal likelihood computation for model selection and hypothesis testing: an extensive review.

On Equivariant Model Selection through the Lens of Uncertainty Marginal likelihood computation for model selection and hypothesis testing: an extensive review

Reference 32

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Observation a980f49b-763f-43b8-bf11-62d17b10d226 · outbound

This paper cites Bayesian model selection, the marginal likelihood, and generalization.

On Equivariant Model Selection through the Lens of Uncertainty Bayesian model selection, the marginal likelihood, and generalization

Reference 33

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Observation 34dbc13c-cf0f-40ea-aa1b-7d3d2867b244 · outbound

This paper cites On the Benefits of Invariance in Neural Networks.

On Equivariant Model Selection through the Lens of Uncertainty On the Benefits of Invariance in Neural Networks

Reference 34

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This paper cites A practical bayesian framework for backpropagation networks.

On Equivariant Model Selection through the Lens of Uncertainty A practical bayesian framework for backpropagation networks

Reference 35

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Observation e55426f4-fc63-49ad-90d7-26cd9d3b8530 · outbound

This paper cites Information theory, inference and learning algorithms.

On Equivariant Model Selection through the Lens of Uncertainty Information theory, inference and learning algorithms

Reference 36

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Observation 0a82165a-2703-459c-9065-c65f8d5d98dc · outbound

This paper cites Forecasting and uncertainty: A survey.

On Equivariant Model Selection through the Lens of Uncertainty Forecasting and uncertainty: A survey

Reference 37

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This paper cites Uncertainty quantification in drug design.

On Equivariant Model Selection through the Lens of Uncertainty Uncertainty quantification in drug design

Reference 38

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This paper cites On genuine invariance learning without weight-tying.

On Equivariant Model Selection through the Lens of Uncertainty On genuine invariance learning without weight-tying

Reference 39

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Observation 0b46554a-02a7-4483-abf3-c8eef7e275b3 · outbound

This paper cites A new vector partition of the probability score.

On Equivariant Model Selection through the Lens of Uncertainty A new vector partition of the probability score

Reference 40

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Observation ec6e98a7-fdd1-40eb-91d7-b3bdb25097b9 · outbound

This paper cites Measuring calibration in deep learning.

On Equivariant Model Selection through the Lens of Uncertainty Measuring calibration in deep learning

Reference 41

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Observation a59d28ea-72e7-4882-a8a7-3261dffc1672 · outbound

This paper cites Approximation-generalization trade-offs under (approximate) group equivariance.

On Equivariant Model Selection through the Lens of Uncertainty Approximation-generalization trade-offs under (approximate) group equivariance

Reference 42

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This paper cites From a point cloud to a simulation model—bayesian segmentation and entropy based uncertainty estimation for 3d modelling.

On Equivariant Model Selection through the Lens of Uncertainty From a point cloud to a simulation model—bayesian segmentation and entropy based uncertainty estimation for 3d modelling

Reference 43

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Observation de14fea9-33ab-46a5-b5dd-fc12ddd1a78d · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.

On Equivariant Model Selection through the Lens of Uncertainty Quantum chemistry structures and properties of 134 kilo molecules

Reference 44

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Observation aa528ece-7fb3-4f8c-8ae4-c69d93bc6a1c · outbound

This paper cites Occam's razor.

On Equivariant Model Selection through the Lens of Uncertainty Occam's razor

Reference 45

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Observation 11267041-8b3b-4daf-8240-733876dbf10e · outbound

This paper cites Learning partial equivariances from data.

On Equivariant Model Selection through the Lens of Uncertainty Learning partial equivariances from data

Reference 46

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Observation ace2af5c-d38e-4d2f-978f-3cb582861b2b · outbound

This paper cites Least ambiguous set-valued classifiers with bounded error levels.

On Equivariant Model Selection through the Lens of Uncertainty Least ambiguous set-valued classifiers with bounded error levels

Reference 47

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This paper cites Last layer marginal likelihood for invariance learning.

On Equivariant Model Selection through the Lens of Uncertainty Last layer marginal likelihood for invariance learning

Reference 48

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Observation 00c17487-7b9c-4927-a9af-78efde96e5b1 · outbound

This paper cites A tutorial on conformal prediction.

On Equivariant Model Selection through the Lens of Uncertainty A tutorial on conformal prediction

Reference 49

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Observation f88a59d2-5c06-4148-b19b-c8b86ef8c837 · outbound

This paper cites Classifier calibration: a survey on how to assess and improve predicted class probabilities.

On Equivariant Model Selection through the Lens of Uncertainty Classifier calibration: a survey on how to assess and improve predicted class probabilities

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.461684Z

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Observation 69d8c9c6-6695-4e45-8920-c3783bb98d88 · outbound

This paper cites Evidential deep learning for guided molecular property prediction and discovery.

On Equivariant Model Selection through the Lens of Uncertainty Evidential deep learning for guided molecular property prediction and discovery

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.443696Z

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Observation 9c0fad21-4b9e-4ce8-83e3-f240ce44c025 · outbound

This paper cites Probing Equivariance and Symmetry Breaking in Convolutional Networks.

On Equivariant Model Selection through the Lens of Uncertainty Probing Equivariance and Symmetry Breaking in Convolutional Networks

Reference 52

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Observation 49517b85-34dd-45d6-b786-c3bcdb0a06f6 · outbound

This paper cites van der Linden, Alejandro Garc \' a-Castellanos, Sharvaree Vadgama, Thijs Kuipers, and Erik J.

On Equivariant Model Selection through the Lens of Uncertainty van der Linden, Alejandro Garc \' a-Castellanos, Sharvaree Vadgama, Thijs Kuipers, and Erik J

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.427806Z

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Observation f34b103f-a1c9-4d61-ab70-160eb3967641 · outbound

This paper cites van der Linden, Alexander Timans, and Erik J.

On Equivariant Model Selection through the Lens of Uncertainty van der Linden, Alexander Timans, and Erik J

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.411536Z

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Observation 1e1a0190-d06a-404a-91d4-ceb70b800bdb · outbound

This paper cites Learning layer-wise equivariances automatically using gradients.

On Equivariant Model Selection through the Lens of Uncertainty Learning layer-wise equivariances automatically using gradients

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.395066Z

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

Observation 7959e5a1-3f66-412d-983e-62304c0d2325 · outbound

This paper cites Learning invariances using the marginal likelihood.

On Equivariant Model Selection through the Lens of Uncertainty Learning invariances using the marginal likelihood

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.378280Z

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

Observation b69ba295-163d-4a14-83bb-720f934f65af · outbound

This paper cites General e(2)-equivariant steerable cnns.

On Equivariant Model Selection through the Lens of Uncertainty General e(2)-equivariant steerable cnns

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T18:49:05.227422Z

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Unavailable: canonical work link unavailable.

Observation 06ba49d0-9265-49ba-a8c5-4a3af04a14d1 · outbound

This paper cites a ger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, and Stephan G \.

On Equivariant Model Selection through the Lens of Uncertainty a ger, Nicholas Gao, Bertrand Charpentier, Mohamed Amine Ketata, and Stephan G \

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.350687Z

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

Observation a72d5ffb-98ea-4800-8083-15eb8d26a557 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

On Equivariant Model Selection through the Lens of Uncertainty 3d shapenets: A deep representation for volumetric shapes

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:49:05.333810Z

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.

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