Probabilistic Deep Learning

26 papers with code • 0 benchmarks • 5 datasets

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Most implemented papers

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

MIMBCD-UI/prototype-multi-modality 7 Apr 2020

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

asharakeh/probdet 13 Jan 2021

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 15 Jan 2019

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 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

stefanradev93/BayesFlow 16 Dec 2021

Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains.

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness

google/uncertainty-baselines 1 May 2022

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 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

itaybenou/DeepTract 12 Dec 2018

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography.

Hybrid Models with Deep and Invertible Features

MarcoRiggirello/diglm 7 Feb 2019

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).