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
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
PredictiveIntelligenceLab/CADGMs
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We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems.
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
google-research/google-research
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NeurIPS 2019
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.
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.
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 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.
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
sergeyprokudin/deep_direct_stat
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ECCV 2018
However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography.
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i. e. a normalizing flow).
