Bayesian interpretation of deep ensembles improves uncertainty quantification.
problem Improving uncertainty estimation in deep learning models.
method Viewing deep ensembles as an approximate Bayesian method and specifying corresponding assumptions.
result Improved approximation leads to larger epistemic uncertainty, potentially more reliable predictions.
Bayesian deep ensembles improve prediction accuracy in various settings.
problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.
Unified theory linking Bayesian and ensemble methods in deep learning.
problem Uncertainty quantification in deep learning.
method Reformulating optimisation as convex optimisation in probability measures, studying Wasserstein gradient flows.
result Unified theory explaining success of deep ensembles over variational inference.
Deep ensembles mimic Bayesian averaging with learned priors.
problem Quantifying uncertainty in neural networks.
method Showed deep ensembles perform exact Bayesian averaging with an implicitly learned data-dependent prior.
result Deep ensembles are Bayesian and provide an explanation for their strong empirical performance.
Repulsive deep ensembles improve diversity and Bayesian inference.
problem Challenges in maintaining diversity among ensemble members trained independently.
method Introducing a repulsive term in the update rule of deep ensembles.
result Training dynamics of repulsive ensembles follow a Wasserstein gradient flow of KL divergence with the true posterior.
A new stopping rule based on E-values helps efficiently use sampling in Bayesian Deep Ensembles.
problem How long should sampling continue in Bayesian Deep Ensembles to yield significant improvements?
method Formulated as a sequential anytime-valid hypothesis test, using E-values to decide when to stop sampling.
result Only a fraction of the full-chain budget is often required for significant improvements.
This paper connects RND, deep ensembles, and Bayesian inference, providing a unified theoretical perspective.
problem Uncertainty quantification in deep learning models.
method Analysis of Random Network Distillation (RND) within the neural tangent kernel framework.
result The uncertainty signal from RND is equivalent to the predictive variance of a deep ensemble and can be made to mirror the centered posterior predictive distribution of Bayesian inference.
Deep ensembles outperform deep ensembles of Bayesian neural networks on in-distribution data.
problem Improving model calibration and uncertainty quantification in Bayesian Neural Networks.
method Systematic investigation of deep ensembles of Bayesian Neural Networks across various datasets and architectures.
result Deep ensembles consistently outperform deep ensembles of Bayesian neural networks on in-distribution data.
Improved neural network ensembles using Stein Variational Newton updates.
problem Lack of efficient second-order information in current ensemble methods.
method Proposes a novel approximate Bayesian inference method integrating Stein Variational Newton updates with scalable Hessian approximations.
result Significantly faster convergence and more accurate posterior distribution approximations.
Bayesian inference using stochastic neural networks ensembles.
problem Approximating Bayesian posterior distributions.
method Formulate stochastic ensembles of neural networks, train with variational inference, and evaluate using Monte Carlo dropout.
result Stochastic ensembles provide more accurate posterior estimates than other methods.
Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in p…
Stein variational neural network ensembles improve diversity and uncertainty estimation.
problem Lack of proper Bayesian justification and diversity guarantees in deep neural network ensembles.
method Particle-based inference methods, specifically Stein variational gradient descent (SVGD), operating in weight space, function space, and hybrid settings.
result SVGD methods improve diversity and uncertainty estimation, approaching the true Bayesian posterior more closely.
MixupMP improves uncertainty quantification in neural networks using data augmentation.
problem Uncertainty quantification in deep learning models.
method MixupMP constructs a more realistic predictive distribution using data augmentation techniques.
result MixupMP achieves superior predictive performance and uncertainty quantification on various image classification datasets.
SBMC method improves uncertainty estimation in deep learning models.
problem Improving uncertainty quantification in deep learning models.
method A scalable Bayesian Monte Carlo method using a model and parallel SMC/MCMC algorithm.
result SBMC achieves comparable or better accuracy and improved uncertainty quantification compared to state-of-the-art methods.
New algorithm reduces overfitting in neural networks.
problem Overfitting in neural networks.
method Integrates SMC with SGHMC for mini-batch sampling.
result SMCSGHMC outperforms SGD and deep ensembles.
Unified predictive uncertainty disentangled using deep split ensembles.
problem Understanding and quantifying uncertainty in NNs for real-world applications.
method Deep split ensemble approach using multivariate Gaussian mixture model.
result Inherently well-calibrated models with high flexibility to group features.
Simple mode exploration methods do not improve performance in neural networks.
problem Improving predictive probabilities in neural networks.
method Exploring local regions around diverse solutions using simple methods.
result Simple mode exploration methods do not improve performance.
This paper examines how to calibrate ensemble members for better prediction accuracy.
problem Improper calibration of deep neural networks leads to unreliable probability estimates.
method Theoretical analysis and empirical evaluation on CIFAR-100 dataset.
result Well-calibrated ensemble members do not guarantee a well-calibrated ensemble prediction, but a well-calibrated ensemble prediction cannot exceed the average performance of its members.
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN). We do so by incorporating a KL divergence penalty term into the training objective of an ensemble, derived from the evidence lower bound used in var…
Proposes a new method for ensembling neural subnetworks.
problem Computational expense and limited flexibility of traditional deep ensembles.
method Sequential Bayesian neural subnetwork ensembling.
result Outperforms traditional ensembles in various metrics.
Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea …
Paper presents a Bayesian-decision-theory framework for long-tailed classification.
problem Heavy imbalance and asymmetric misprediction costs in long-tailed datasets.
method Bayesian-decision-theory perspective, unifying re-balancing and ensemble methods.
result Improves accuracy for all classes, especially tails, with provably optimal decisions.
Bayesian optimization uses BNNs as efficient surrogate models for expensive function evaluations.
problem Optimizing expensive objective functions using Gaussian process surrogates.
method Study of Bayesian neural networks (BNNs) as alternatives to standard Gaussian process (GP) surrogates for optimization.
result Infinite-width BNNs are particularly promising, especially in high dimensions.
Combines Laplace approximations of deep networks for better uncertainty quantification.
problem Overconfident predictions on outliers in deep learning models.
method Gaussian mixture model posterior using weighted sum of Laplace approximations of pre-trained deep networks.
result Mitigates overconfidence 'far away' from training data.
Bayesian learning made scalable with posteriors library.
problem Computational challenges in Bayesian learning with modern models.
method Introducing posteriors library and tempered MCMC.
result Bayesian approximations are useful and scalable.
Bayesian deep learning improves neural network accuracy and generalization.
problem Improving accuracy and calibration of deep neural networks.
method Bayesian marginalization and deep ensembles to approximate marginalization, and tempering for calibrating predictive distributions.
result Bayesian approaches improve deep neural networks' accuracy and generalization.
Deep learning models predict mutual funds' performance better than traditional methods.
problem Predicting mutual funds' performance accurately.
method Deep learning models (LSTM, GRUs) trained with Bayesian optimization and ensemble methods.
result Ensemble method of LSTM and GRUs achieves the highest accuracy in forecasting mutual funds' Sharpe ratios.
In image classification tasks, the ability of deep CNNs to deal with complex image data has proven to be unrivalled. However, they require large amounts of labeled training data to reach their full potential. In specialised domains such as healthcare, labeled data can be difficult and expensive to obtain. Active Learni…
Bayesian neural networks show complex posterior distributions that HMC can capture effectively.
problem Understanding and approximating the high-dimensional, non-convex posterior of Bayesian neural networks.
method Full-batch Hamiltonian Monte Carlo (HMC) on modern architectures.
result HMC provides a robust and comparable representation of the BNN posterior, with significant performance gains over standard training and deep ensembles.
BODE enhances deep neural network predictions and uncertainty quantification in safety modeling.
problem Uncertainty in deep neural network predictions for safety-critical applications.
method Bayesian optimization combined with deep ensembles (BODE).
result BODE reduces total uncertainty by over 30% compared to a manually tuned baseline ensemble.
Bayesian methods improve text annotation quality.
problem Inconsistent and unreliable human annotations in natural language processing.
method Two semi-supervised Bayesian methods: a deep learning model and an ensemble method.
result Bayesian methods enhance the reliability and performance of BERT models.
Bayesian deep learning ensemble improves pneumonia diagnosis accuracy.
problem Manual, time-consuming pneumonia diagnosis with high inter-observer variability.
method Multi-level ensemble classification system using Bayesian Deep Learning.
result Accuracy of 98.06% in differentiating four pathologies.
Enhances PlaNet for better planning in uncertain environments.
problem Improving deep planning networks for partially observable environments.
method Incorporates Bayesian inference to handle uncertainty in latent models and action candidates.
result Consistently improves asymptotic performance on continuous control tasks.
Efficient BNNs learn latent distributions for robust uncertainty quantification.
problem Improving robustness and uncertainty of deep neural networks.
method LP-BNN uses VAEs to learn latent distributions of BNN parameters, enabling efficient ensembles.
result LP-BNN achieves competitive results in image classification, semantic segmentation, and out-of-distribution detection.
New method for Bayesian neural networks reduces inference difficulty.
problem Difficulty in sample-based inference for Bayesian neural networks.
method Embracing mode-connectedness to link overparameterization and sampling difficulty.
result Practical guidelines and deep ensemble approach for effective SBI.
Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provide an effective solution to this. Despite being theoretically principled, BNNs require approximations to be applied to large-scale problems, …
Single model estimates ensemble uncertainty efficiently.
problem Efficient uncertainty quantification in deep learning models.
method Contextual similarity distillation, approximating ensemble variance with a single model.
result Single model estimates predictive variance with a single forward pass.
LLEB uses a learned prior to quantify neural network uncertainty.
problem Quantifying uncertainty in neural network predictions.
method LLEB uses a learnable prior as a normalizing flow to maximize the evidence lower bound.
result LLEB performs on par with existing approaches in uncertainty quantification.
This paper explores BDL hyperparameters for robust polynomial mapping with noise.
problem Designing BDL hyperparameters for robust function mapping with uncertainty quantification.
method Mapping Bayesian connectionist representations to polynomials of varying orders and noise types.
result Optimal network depth and ensemble size for prediction and uncertainty quantification.
We present a generative approach to classify scarcely observed longitudinal patient trajectories. The available time series are represented as tensors and factorized using generative deep recurrent neural networks. The learned factors represent the patient data in a compact way and can then be used in a downstream clas…
This work evaluates uncertainty in deep Gaussian processes.
problem Uncertainty quantification in deep Gaussian processes.
method Hierarchical deep Gaussian processes (DGPs) and Deep Sigma Point Processes (DSPPs) evaluated on regression and classification tasks.
result DSPPs provide strong in-distribution calibration but are less robust under distribution shift compared to ensembles.
A method to reduce memory usage in deep learning models by adding inducing weights.
problem Memory inefficiency in Bayesian neural networks and deep ensembles.
method Augmenting the weight matrix with inducing weights and using Matheron's conditional Gaussian sampling rule.
result Reduces parameter size to 24.3% of a single neural network while maintaining competitive performance.
Proposes optimizing neural network ensemble diversity in feature space.
problem Improving diversity in neural network ensembles while maintaining performance.
method Optimizes particles in the feature space of a specific intermediate layer.
result Significantly outperforms Deep Ensembles on various metrics.
This paper improves ensemble learning for vision tasks by encouraging diversity in predictions.
problem Generating effective ensembles of neural networks for multi-modal data.
method Explicitly optimize a diversity inducing adversarial loss for learning stochastic latent variables.
result Significant improvements in classification accuracy and out-of-distribution detection compared to baselines.
Bayesian deep learning improves accuracy and calibration without sacrificing scalability.
problem Bayesian inference's potential for deep neural networks.
method Marginalization over optimization, using neural networks' inherent structure and inductive biases.
result Improvements in accuracy and calibration compared to standard training methods.
Paper assesses adversarial robustness of MCMC and BDK methods for deep Bayesian networks.
problem Assessing adversarial robustness of deep neural networks under MCMC and BDK approximations.
method Characterizes robustness of MCMC and BDK methods to FGSM and PGD attacks.
result Full MCMC-based inference shows excellent robustness, outperforming standard point estimation.
Bayesian symbolic regression automates model discovery from data.
problem Learning closed-form mathematical models from data using heuristic methods.
method Probabilistic approach to symbolic regression, connecting to information theory and statistical physics.
result Probabilistic approach provides model plausibility and performance guarantees.
Bayesian Neural Networks improve geophysical model ensembles with reduced uncertainty.
problem Improving geophysical model projections and uncertainty quantification.
method Developed a Bayesian Neural Network ensemble strategy for geophysical models.
result Bayesian Neural Network ensemble outperforms existing methods in ozone prediction.