Probabilistic Deep Learning
29 papers with code • 0 benchmarks • 5 datasets
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Latest papers
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.
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 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.
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle.
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.
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
ByungKwanLee/Hierarchical-Bayesian-Defense
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Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense.
A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
yingtaoluo/Probabilistic-density-network
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Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures.
Learning Monocular Dense Depth from Events
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames.
Olympus: a benchmarking framework for noisy optimization and experiment planning
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.
