Papers Probabilistic Deep Learning
“Probabilistic Deep Learning” 태그가 달린 논문 79편 · 필터 해제
Decadal sink-source shifts of forest aboveground carbon since 1988
As enduring carbon sinks, forest ecosystems are vital to the terrestrial carbon cycle and help moderate global warming. However, the long-term dynamics of aboveground carbon (AGC) in forests and their sink-source transit…
Probabilistic Deep LearningForecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models
We present a novel framework for high-resolution forecasting of residential heating and electricity demand using probabilistic deep learning models. We focus specifically on providing hourly building-level electricity an…
Probabilistic Deep LearningUnfolding AlphaFold's Bayesian Roots in Probability Kinematics
We present a novel theoretical interpretation of AlphaFold1. The seminal breakthrough of AlphaFold1 in protein structure prediction by deep learning relied on a learned potential energy function, in contrast to the later…
Probabilistic Deep LearningProtein Structure PredictionQuantification of Uncertainties in Probabilistic Deep Neural Network by Implementing Boosting of Variational Inference
Modern neural network architectures have achieved remarkable accuracies but remain highly dependent on their training data, often lacking interpretability in their learned mappings. While effective on large datasets, the…
Probabilistic Deep LearningUncertainty QuantificationVariational InferenceMicrocanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Despite recent advances, sampling-based inference for Bayesian Neural Networks (BNNs) remains a significant challenge in probabilistic deep learning. While sampling-based approaches do not require a variational distribut…
NavigateProbabilistic Deep LearningUncertainty QuantificationFast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation
Reconstructing the structure of thin films and multilayers from measurements of scattered X-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflecto…
Probabilistic Deep LearningProbabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price Prediction
Forecasting the price of Bitcoin (BTC) with precision is a complex endeavor, given the market’s inherent uncertainty and volatility, influenced by a diverse range of parameters. This research is driven by the central goa…
Probabilistic Deep LearningStock Price PredictionTime Series AnalysisTransfer LearningDetecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation program (simulation-based inference; SBI).…
Bayesian InferenceDecision MakingProbabilistic Deep LearningIntegrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification
Full-Waveform Inversion (FWI) is a nonlinear iterative seismic imaging technique that, by reducing the misfit between recorded and predicted seismic waveforms, can produce detailed estimates of subsurface geophysical pro…
Deep LearningProbabilistic Deep LearningSeismic ImagingUncertainty QuantificationInterference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network
The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due…
DecoderProbabilistic Deep LearningInformed Spectral Normalized Gaussian Processes for Trajectory Prediction
Prior parameter distributions provide an elegant way to represent prior expert and world knowledge for informed learning. Previous work has shown that using such informative priors to regularize probabilistic deep learni…
Autonomous DrivingContinual LearningGaussian ProcessesPrediction+3Forecasting VIX using Bayesian Deep Learning
Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for i…
Deep LearningProbabilistic Deep LearningStochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations. Stochastically driven f…
Numerical IntegrationProbabilistic Deep LearningDeep Gaussian Mixture Ensembles
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data g…
Deep LearningProbabilistic Deep LearningVariational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbal…
Probabilistic Deep LearningregressionUncertainty QuantificationKernel Density Matrices for Probabilistic Deep Learning
This paper introduces a novel approach to probabilistic deep learning, kernel density matrices, which provide a simpler yet effective mechanism for representing joint probability distributions of both continuous and disc…
Deep LearningDensity Estimationimage-classificationImage Classification+2Uncertainty Voting Ensemble for Imbalanced Deep Regression
Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by the densely covered regions of the targe…
Probabilistic Deep LearningregressionOn Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign high confidence to low-quality predictions…
Probabilistic Deep LearningComparison of Probabilistic Deep Learning Methods for Autism Detection
Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is now widespread in the world. ASD persists throughout the life of an individual, impacting the way they behave and communicate, resulting to notab…
Autism detectionDeep LearningProbabilistic Deep LearningBenchmarking Probabilistic Deep Learning Methods for License Plate Recognition
Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forens…
BenchmarkingDeep LearningLicense Plate RecognitionProbabilistic Deep Learning+2