Qualitative analysis of MC dropout for NN model uncertainty.
problem Measuring uncertainty in neural network models.
method Mathematical formulation of Monte Carlo dropout and its benefits/costs in NN models.
result Potential benefits and associated costs of using MC dropout in NN models.
A fast single-shot MC dropout method for neural networks.
problem Inability of DNNs to provide uncertainty measures for new situations.
method Analytically approximates MC dropout for fully connected networks.
result Approach preserves BDNN advantages while being faster.
MC Dropout is re-evaluated as not Bayesian, affecting predictive posterior and multimodality.
problem MC Dropout's Bayesian properties and predictive posterior are questioned.
method Re-evaluation of MC Dropout's properties, including a new VI engine in pytorch.
result MC Dropout does not produce a faithful Bayesian predictive posterior.
Enhances uncertainty estimation in neural networks using Dirichlet-based MC Dropout.
problem Deterministic predictions without uncertainty estimates in neural networks.
method Integrates Dirichlet-based framework within Monte Carlo Dropout.
result Improves quality of uncertainty estimates in deep learning models.
MC-CP combines adaptive MC dropout with conformal prediction for robust uncertainty quantification.
problem Deploying deep learning models in safety-critical applications requires reliable confidence estimates.
method MC-CP integrates adaptive Monte Carlo dropout with conformal prediction to improve model performance.
result MC-CP significantly outperforms state-of-the-art UQ methods in both classification and regression tasks.
Batch normalisation doesn't affect variational inference but fails for larger batch sizes.
problem Failure of Monte Carlo Batch Normalisation (MCBN) for capturing epistemic uncertainty in larger batch sizes.
method Investigated MCBN as an approximate inference technique for Bayesian neural networks, showing its limitations and providing insights for improvement.
result For larger batch sizes, MCBN fails to capture epistemic uncertainty, requiring the batch size to be a variational parameter.
A fast method for neural networks that provides uncertainty measures.
problem Inability of neural networks to provide reliable uncertainty measures for new situations.
method Moment propagation (MP) for analytically approximating MC dropout.
result Single-shot MC dropout approximation as fast as NNs with similar uncertainty measures.
Simplifies neural regression by combining two sub-networks for predictions and uncertainties.
problem Neural networks underestimate uncertainty, leading to overly confident predictions.
method Extends IRLS to a two-sub-network approach with shared representations and complementary loss functions.
result Proposed network is simpler to implement and more robust to uncertainty variations.
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is kn…
Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.
problem High aleatoric uncertainty and visual ambiguity in facial emotion recognition.
method Bayesian Neural Networks approximated using MC-Dropout, MC-DropConnect, or Ensemble methods.
result Bayesian Neural Networks produce more human-like output probabilities.
Study on MC dropout in wide neural networks and its convergence to Gaussian processes.
problem Understanding the behavior of Monte Carlo dropout in wide neural networks.
method Rigorously studied the limiting distribution of wide untrained NNs under dropout, proving convergence to Gaussian processes. Investigated correlations and non-Gaussian behavior in finite width NNs.
result Wide untrained neural networks under dropout converge to Gaussian processes for fixed sets of weights and biases.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
QUAM improves uncertainty quantification in deep learning models.
problem Estimating epistemic uncertainty in deep learning models.
method QUAM identifies regions with high divergence between predictions and a reference model.
result QUAM has lower approximation error of epistemic uncertainty compared to previous methods.
We propose Radial Bayesian Neural Networks (BNNs): a variational approximate posterior for BNNs which scales well to large models while maintaining a distribution over weight-space with full support. Other scalable Bayesian deep learning methods, like MC dropout or deep ensembles, have discrete support-they assign zero…
A cubing strategy identifies stable hyperparameter regions for uncertainty quantification in spatial deep learning.
problem Uncertainty quantification in spatial deep learning models.
method Cubing-based diagnostic framework to recursively partition hyperparameter space and evaluate regions using scoring rules.
result Our approach produces competitive or superior predictive intervals compared to a statistical baseline model.
We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby …
Advances in deep neural network (DNN) based molecular property prediction have recently led to the development of models of remarkable accuracy and generalization ability, with graph convolution neural networks (GCNNs) reporting state-of-the-art performance for this task. However, some challenges remain and one of the …
Study on Monte-Carlo dropout for uncertainty estimation in neural networks.
problem Variability in uncertainty estimation quality through Monte-Carlo dropout.
method Analysis of dropout behavior in neural networks.
result Observation of interesting properties for uncertainty estimation.
Measuring uncertainty is a promising technique for detecting adversarial examples, crafted inputs on which the model predicts an incorrect class with high confidence. But many measures of uncertainty exist, including predictive en- tropy and mutual information, each capturing different types of uncertainty. We study th…
Two main obstacles preventing the widespread adoption of variational Bayesian neural networks are the high parameter overhead that makes them infeasible on large networks, and the difficulty of implementation, which can be thought of as "programming overhead." MC dropout [Gal and Ghahramani, 2016] is popular because it…
Develops a multilevel Monte Carlo framework with dropout for efficient uncertainty quantification.
problem Efficiently quantify uncertainty in complex models using dropout.
method Integrates multilevel Monte Carlo with Monte Carlo dropout, creating coupled estimators to reduce variance.
result Demonstrates significant variance reduction and efficiency gains over single-level Monte Carlo dropout.
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent. Many calibration methods in the literature have been proposed to improve the predictive uncertainty of DNNs which are generally not well-calibrated. H…
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physica…
Paper proposes a method to improve prediction intervals for neural networks.
problem Improving prediction intervals for neural network models.
method Adapting extremely randomized trees to neural networks to create ensembles.
result The method yields gains in out-of-sample accuracy and is superior to existing methods.
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient descent (SGD) iterates with a modified learning rate schedule, has recently been sho…
We prove, under two sufficient conditions, that idealised models can have no adversarial examples. We discuss which idealised models satisfy our conditions, and show that idealised Bayesian neural networks (BNNs) satisfy these. We continue by studying near-idealised BNNs using HMC inference, demonstrating the theoretic…
Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify pred…
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We for…
BayesDLL offers a PyTorch library for Bayesian deep learning with large models.
problem Bayesian inference for large-scale deep networks.
method Variational inference, MC-dropout, stochastic-gradient MCMC, Laplace approximation.
result BayesDLL can handle Vision Transformers and pre-trained model weights as priors.
We introduce vine computational graphs for efficient ML integration of vine copulas.
problem Integrating vine copulas into modern machine learning pipelines.
method Developed vine computational graphs and algorithms for conditional sampling, scheduling, and structure construction.
result Gradient flow through vine copulas improves performance in machine learning models.
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive unc…
Bayesian active learning improves stress and affect detection on wearable devices.
problem Handling unlabeled data in real-time for stress and affect detection.
method Bayesian Neural Networks with Monte-Carlo Dropout and suitable acquisition functions.
result Framework achieves significant efficiency boost and low number of acquired pool points.
Bayesian approach improves image captioning quality metrics.
problem Improving image captioning quality metrics.
method Bayesian Self-Critical Sequence Training (B-SCST) with Monte Carlo dropout approximate variational inference.
result B-SCST improves CIDEr-D scores on various image captioning datasets.
The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.
problem Accurate treatment effect estimation requires reliable uncertainty measures to locate model failures.
method Layer-wise variance decomposition using Monte Carlo Dropout in twin networks.
result The encoder component dominates under distributional shift, providing a practical diagnostic for data collection.
Paper presents a method to disrupt deep uncertainty estimation without affecting accuracy.
problem Uncertainty estimation in deep neural networks for risk-sensitive applications.
method A novel attack that cripples uncertainty estimation without reducing accuracy.
result The attack causes the network to be more confident in incorrect predictions than correct ones.
Paper reduces uncertainty in predictive models using neural networks and Gaussian processes.
problem High epistemic uncertainty in predictive models.
method Adaptive sampling approach with prediction interval-generation neural networks and Gaussian processes.
result Method consistently converges faster to minimum epistemic uncertainty levels.
Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently \emph{emulate} an ensemble of models for classification by paramete…
This research improves model interpretability and uncertainty estimation for deep learning models on non-iid data.
problem Improving interpretability and uncertainty estimation for deep learning models on non-iid data.
method 4 UQ approaches (BNN, SWAG, MC dropout, ensemble) applied to ARMED MEDL models.
result Ensemble approaches, especially with 90% subsampling, provide best performance in prediction and uncertainty estimation.
DP-BNNs improve accuracy, privacy, and reliability in neural networks.
problem Balancing privacy and accuracy in neural networks.
method Proposed three DP-BNNs: DP-SGLD, DP-BBP, and DP-MC Dropout.
result DP-SGLD achieves high accuracy under strong privacy guarantees.
Favour speeds up variance estimation in BNNs, making them practical for performance-critical tasks.
problem Slow inference in Bayesian Neural Networks (BNNs) hinders their adoption.
method A fast algorithm for updating a diagonal-plus-low-rank matrix approximation under various operations.
result Favour is as fast as 2-3 inference samples but matches the performance of 10-100 samples.
Deep active inference agents learn complex environments using Monte-Carlo methods.
problem Understanding and modeling biological intelligence in complex, continuous state-spaces.
method Neural architecture for deep active inference agents using multiple forms of Monte-Carlo sampling.
result Deep active inference agents can learn environmental dynamics and plan future actions.
Study evaluates quality of uncertainty estimates for neural networks.
problem Lack of principled assessment methods for evaluating uncertainty quality in deep learning.
method Statistical methods of frequentist interval coverage, interval width, and expected calibration error.
result Different UQ methods produce markedly different quality uncertainty estimates.