Probabilistic Deep Learning

22 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.

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}.

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).

DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

grockious/deepsynth 22 Nov 2019

This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

zalandoresearch/pytorch-ts ICLR 2021

In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.

Olympus: a benchmarking framework for noisy optimization and experiment planning

aspuru-guzik-group/olympus 8 Oct 2020

Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.