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Probabilistic Deep Learning

Papers

Showing 125 of 79 papers

TitleStatusHype
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing FlowsCode2
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear ModulationCode1
A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-StructuresCode1
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality ForecastingCode1
Learning Monocular Dense Depth from EventsCode1
Olympus: a benchmarking framework for noisy optimization and experiment planningCode1
Graph-based Thermal-Inertial SLAM with Probabilistic Neural NetworksCode1
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object DetectorsCode1
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensemblesCode1
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational InferenceCode1
BreastScreening: On the Use of Multi-Modality in Medical Imaging DiagnosisCode1
Probabilistic Deep Learning for Electric-Vehicle Energy-Use PredictionCode1
Transductive Decoupled Variational Inference for Few-Shot ClassificationCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Causal Discovery from Conditionally Stationary Time Series0
A high-resolution canopy height model of the Earth0
Forecasting VIX using Bayesian Deep Learning0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation0
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans0
Forecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models0
Bayesian Neural Networks: Essentials0
Deep Gaussian Mixture Ensembles0
Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation0
A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra0
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