Pinned Repositories
bayesian-neural-network-blogpost
Building a Bayesian deep learning classifier
cFlow
This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra Selvan et al. 2020
examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Flutter_Stocks
项目使用Flutter进行开发，同时支持Andriod与iOS。 支持财经新闻阅读、实时大盘指数、实时沪深行情、k线查看、登录、网页查看、侧边栏、系統分享、微信分享等功能
lightning
Deep learning framework to train, deploy, and ship AI products Lightning fast.
LLM101n
LLM101n: Let's build a Storyteller
mDKL
The PyTorch code for the publication "Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning" on ICHI 2021 (9th IEEE International Conference on Healthcare Informatics)
MICCAI_QUBIQ_21
Holistic network for quantifying uncertainties in medical images. Work presented at the MICCAI BrainLes 2021 workshop.
MLExp
Probabilistic-Deep-Learning-with-TensorFlow
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.
Yjinhua's Repositories
Yjinhua/bayesian-neural-network-blogpost
Building a Bayesian deep learning classifier
Yjinhua/cFlow
This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra Selvan et al. 2020
Yjinhua/examples
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Yjinhua/Flutter_Stocks
项目使用Flutter进行开发，同时支持Andriod与iOS。 支持财经新闻阅读、实时大盘指数、实时沪深行情、k线查看、登录、网页查看、侧边栏、系統分享、微信分享等功能
Yjinhua/lightning
Deep learning framework to train, deploy, and ship AI products Lightning fast.
Yjinhua/LLM101n
LLM101n: Let's build a Storyteller
Yjinhua/mDKL
The PyTorch code for the publication "Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning" on ICHI 2021 (9th IEEE International Conference on Healthcare Informatics)
Yjinhua/MICCAI_QUBIQ_21
Holistic network for quantifying uncertainties in medical images. Work presented at the MICCAI BrainLes 2021 workshop.
Yjinhua/MLExp
Yjinhua/Probabilistic-Deep-Learning-with-TensorFlow
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.
Yjinhua/trace
Modern crypto portfolio & market explorer. Built with @Flutter
Yjinhua/uncertainty-deep-image-prior
On Uncertainty Estimation in Medical ImageDenoising with Bayesian Deep Image Prior