Papers Probabilistic Deep Learning
“Probabilistic Deep Learning” 태그가 달린 논문 79편 · 필터 해제
Workload Forecasting of a Logistic Node Using Bayesian Neural Networks
Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays…
Probabilistic Deep LearningProbabilistic Time Series ForecastingA Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
Computed tomography has propelled scientific advances in fields from biology to materials science. This technology allows for the elucidation of 3-dimensional internal structure by the attenuation of x-rays through an ob…
ObjectProbabilistic Deep LearningTransductive Decoupled Variational Inference for Few-Shot Classification
The versatility to learn from a handful of samples is the hallmark of human intelligence. Few-shot learning is an endeavour to transcend this capability down to machines. Inspired by the promise and power of probabilisti…
ClassificationFew-Shot Image ClassificationFew-Shot LearningProbabilistic Deep Learning+1FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to…
Deep LearningMulti-Task LearningProbabilistic Deep LearningUncertainty QuantificationA Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular app…
Data AugmentationDeep LearningProbabilistic Deep LearningUncertainty QuantificationShort-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification…
Decision MakingLoad ForecastingProbabilistic Deep LearningProbabilistic Time Series ForecastingA high-resolution canopy height model of the Earth
The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information …
Decision MakingProbabilistic Deep LearningScene ClassificationVocal Bursts Intensity PredictionA Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
The COVID-19 pandemic has significantly impacted the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesse…
Correlated Time Series ForecastingCOVID-19 ModellingCOVID-19 TrackingInterpretability Techniques for Deep Learning+7Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step approach to enable…
Bayesian InferenceDecision Makingparameter estimationProbabilistic Deep LearningProbabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. Despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that com…
Deep LearningProbabilistic Deep LearningUncertainty QuantificationProbabilistic Deep Learning with Generalised Variational Inference
We study probabilistic Deep Learning methods through the lens of Approximate Bayesian Inference. In particular, we examine Bayesian Neural Networks (BNNs), which usually suffer from multiple ill-posed assumptions such as…
Bayesian InferenceDeep LearningProbabilistic Deep LearningUncertainty Quantification+1Probabilistic Deep Learning for Real-Time Large Deformation Simulations
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information ab…
Deep LearningProbabilistic Deep LearningCausal Discovery from Conditionally Stationary Time Series
Causal discovery, i.e., inferring underlying causal relationships from observational data, has been shown to be highly challenging for AI systems. In time series modeling context, traditional causal discovery methods mai…
Causal DiscoveryCausal InferenceProbabilistic Deep LearningTime Series+1Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
To validate the safety of automated vehicles (AV), scenario-based testing aims to systematically describe driving scenarios an AV might encounter. In this process, continuous inputs such as velocities result in an infini…
Gaussian ProcessesProbabilistic Deep LearningProbabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction
The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel …
Deep LearningPredictionProbabilistic Deep LearningRestricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey
This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We start with the required background on probabilistic graphical models, Markov random fie…
Dimensionality ReductionProbabilistic Deep LearningSurveyHybrid Bayesian Neural Networks with Functional Probabilistic Layers
Bayesian neural networks provide a direct and natural way to extend standard deep neural networks to support probabilistic deep learning through the use of probabilistic layers that, traditionally, encode weight (and bia…
Bayesian InferenceGaussian ProcessesProbabilistic Deep LearningVariational InferenceProbabilistic partition of unity networks: clustering based deep approximation
Partition of unity networks (POU-Nets) have been shown capable of realizing algebraic convergence rates for regression and solution of PDEs, but require empirical tuning of training parameters. We enrich POU-Nets with a …
ClusteringProbabilistic Deep LearningregressionUnityBayesian Neural Networks: Essentials
Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replaceme…
Bayesian InferenceProbabilistic Deep LearningProbabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approac…
Deep LearningGaussian ProcessesProbabilistic Deep Learning