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Probabilistic Deep Learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/EmanuelSommer/MILE

공식 코드

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks

https://github.com/MIMBCD-UI/prototype-multi-modality

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

https://github.com/PredictiveIntelligenceLab/CADGMs

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Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

https://github.com/Vahid-Negahdari/Elastic-Full-Waveforrm-Inversion

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Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification

https://github.com/abdulmajid-murad/deep_probabilistic_forecast

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Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

https://github.com/anujinho/trident

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Transductive Decoupled Variational Inference for Few-Shot Classification

https://github.com/asharakeh/probdet

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Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors

https://github.com/ashfarhangi/covid-19

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A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19

https://github.com/aspuru-guzik-group/olympus

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Olympus: a benchmarking framework for noisy optimization and experiment planning

https://github.com/fagonzalezo/kdm

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Kernel Density Matrices for Probabilistic Deep Learning

https://github.com/fagonzalezo/kdm_for_probabilistic_dl_experiments

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Kernel Density Matrices for Probabilistic Deep Learning

https://github.com/franziska-schirrmacher/lpr-uncertainty

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Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition

https://github.com/google-research/google-research

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Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

https://github.com/google/uncertainty-baselines

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

https://github.com/grockious/deepsynth

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

https://github.com/ira-shokar/stochastic_latent_transformer

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Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets

https://github.com/itaybenou/DeepTract

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DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography

https://github.com/langnico/GEDI-BDL

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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

https://github.com/lmb-freiburg/Multimodal-Future-Prediction

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Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction

https://github.com/marpogaus/stplf-bnf

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Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows

https://github.com/marvinschmitt/ModelMisspecificationBF

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Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks

https://github.com/prs-eth/film-ensemble

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FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

https://github.com/risqiutama/ti-slam

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Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks

https://github.com/sergeyprokudin/deep_direct_stat

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Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

https://github.com/sherryjyc/uvote

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Uncertainty Voting Ensemble for Imbalanced Deep Regression

https://github.com/uzh-rpg/rpg_e2depth

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Learning Monocular Dense Depth from Events

https://github.com/vganapati/ct_pvae

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A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography

https://github.com/wang-ml-lab/variational-imbalanced-regression

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Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing

https://github.com/wnklmx/dsiod

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Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios

https://github.com/yingtaoluo/Probabilistic-density-network

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A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures

https://github.com/zalandoresearch/pytorch-ts

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Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

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