Paper Citation Record · LEDGER

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

As of 19 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:1506.02142.

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pith.paper-citation-record.v1
1506.02142 v6

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measured 35 of 35 standing notices

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measured 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-07-10T06:15:00.866473Z

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Outbound references

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Pith citing papers

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Concrete Problems in AI Safety cites this paper.

Concrete Problems in AI Safety Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 53

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Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach cites this paper.

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 35

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Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy cites this paper.

Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 53

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Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review cites this paper.

Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 12

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Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification cites this paper.

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 5

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ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier cites this paper.

ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 31

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MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification cites this paper.

MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 5

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Testing the Assumptions of Active Learning for Translation Tasks with Few Samples cites this paper.

Testing the Assumptions of Active Learning for Translation Tasks with Few Samples Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 7

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U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster cites this paper.

U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 21

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A Physics-Aware Variational Graph Autoencoder for Joint Modal Identification with Uncertainty Quantification cites this paper.

A Physics-Aware Variational Graph Autoencoder for Joint Modal Identification with Uncertainty Quantification Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 21

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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers cites this paper.

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 51

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Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data cites this paper.

Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 6

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Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training cites this paper.

Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 10

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Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning cites this paper.

Homogeneous Stellar Parameters from Heterogeneous Spectra with Deep Learning Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 23

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Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection cites this paper.

Mixed-Precision Information Bottlenecks for On-Device Trait-State Disentanglement in Bipolar Agitation Detection Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 79

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Inducing Artificial Uncertainty in Language Models cites this paper.

Inducing Artificial Uncertainty in Language Models Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 8

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Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey cites this paper.

Extracting redshifts from 2D slitless spectroscopic images using deep learning for the CSST galaxy survey Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 12

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When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning cites this paper.

When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 7

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Empirical Bayes Conformal Prediction for Vision and Language Models cites this paper.

Empirical Bayes Conformal Prediction for Vision and Language Models Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 9

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VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction cites this paper.

VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Bayesian Inference with Shaped Deep Non-linear MLPs cites this paper.

Bayesian Inference with Shaped Deep Non-linear MLPs Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Probabilistic Precipitation Nowcasting with Rectified Flow Transformers cites this paper.

Probabilistic Precipitation Nowcasting with Rectified Flow Transformers Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 34

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Precision constraints on stellar physics from main sequence detached eclipsing binaries cites this paper.

Precision constraints on stellar physics from main sequence detached eclipsing binaries Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 62

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PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design cites this paper.

PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 65

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ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies cites this paper.

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 42

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Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models cites this paper.

Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 53

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Modelling magnetic material properties with uncertainty-aware neural networks cites this paper.

Modelling magnetic material properties with uncertainty-aware neural networks Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Active Perception for Radio Map Reconstruction in Uncharted 3D Air-Ground Environments cites this paper.

Active Perception for Radio Map Reconstruction in Uncharted 3D Air-Ground Environments Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 33

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MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense cites this paper.

MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defense Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space cites this paper.

Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization cites this paper.

Uncertainty-Aware Deep Learning for the Ly$\alpha$ Forest: CNN-Based Absorber Detection and Characterization Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust cites this paper.

NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification cites this paper.

GRC-ProbNet: Uncertainty-aware Feature Extraction for Cardiovascular Disease Classification Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy cites this paper.

The evolution of AI from image interpretation toward scientific inference in nanoparticle electron microscopy Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers cites this paper.

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

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