Probabilistic Deep Learning
29 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Probabilistic Deep Learning
DENSE
VQA-HAT
ADVIO
CBC
Hotel Sales
Latest papers
Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
ira-shokar/stochastic_latent_transformer
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We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations.
Kernel Density Matrices for Probabilistic Deep Learning
This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and discrete random variables.
Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition
franziska-schirrmacher/lpr-uncertainty
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Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition.
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete.
Transductive Decoupled Variational Inference for Few-Shot Classification
anujinho/trident
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The versatility to learn from a handful of samples is the hallmark of human intelligence.
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
google/uncertainty-baselines
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The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles.
Short-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.
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
To this end, it is essential to develop an interpretable forecast model that supports managerial and organizational decision-making.
Detecting 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.
