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
Most implemented papers
Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction
linas-p/EVDPEP
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International Symposium on Spatial and Temporal Databases 2021
For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use.
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
However, despite the safety criticality of AV testing, metamodels are usually seen as a part of an overall approach, and their predictions are not questioned.
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
abdulmajid-murad/deep_probabilistic_forecast
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Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
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.
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.
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.
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.
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.
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.
