项目作者： probabilisticai

项目描述：
Materials of the Nordic Probabilistic AI School 2019.

高级语言： Jupyter Notebook

项目主页： https://probabilistic.ai

项目地址: git://github.com/probabilisticai/probai-2019.git

创建时间： 2019-06-03T08:44:28Z
项目社区：https://github.com/probabilisticai/probai-2019
开源协议：
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ProbAI 2019

Recorded lectures and tutorials, slides and program code from the very first Nordic Probabilistic AI School (ProbAI 2019).

Lectures and Tutorials

Day 1 (June 3):
- [Lecture, Materials] Introduction to Probabilistic Modelling
- [Tutorial, Materials] Probabilistic Programming
Day 2 (June 4):
- [Lecture, Materials] Variational Inference and Optimization 1
- [Tutorial, Materials] Variational Inference and Probabilistic Programming 1
Day 3 (June 5):
- [Lecture, Materials] Variational Inference and Optimization 2
- [Tutorial, Materials] Variational Inference and Probabilistic Programming 2
Day 4 (June 6):
- [Lecture, Materials] Introduction to Deep Learning & Deep Generative Models
- [Tutorial, Materials] Deep Latent Variable Models for Imputation of Incomplete Data Sets (Imputations with MIWAE)
Day 5 (June 7):
- [Lecture, Materials] Deep Generative Models
- [Tutorial, Materials] Bayesian Sparsification of Neural Networks

Google Colab

If your local computer doesn’t have all the software packages and you are not able to finish the installation, you can try Google Colab.

Start the Google Colab notebook with the following line to install the necessary packages !pip install -q --upgrade pyro-ppl torch.

Day 1 - students_PPLs_Intro.ipynb
Day 1 - students_Bayesian_regression.ipynb
Day 2 - students_simple_model.ipynb
Day 2 - students_lin_reg.ipynb
Day 3 - student_simple_model.ipynb
Day 3 - student_BBVI.ipynb
Day 3 - Bayesian_linear_regression.ipynb
Day 3 - VAE.ipynb
Day 3 - FA.ipynb

Talks

Day 2:
- Evolving Deep Neural Networks by Keith L. Downing
Day 3:
- Value of Information by Jo Eidsvik
- Bayesian Methods for Rank and Preference Data: From Recommender Systems to Cancer Genomics by Valeria Vitelli
Day 4:
- Combining Model and Parameter Uncertainty in BNNs by Aliaksandr Hubin

slides-L1_1647724995598.pdf
introduction_to_probabilistic_modelling_1647724996444.pdf
slides-L2_1647724997236.pdf
evolving_deep_neural_networks_1647724997347.pdf
variational_inference_and_optimization_1_1647724997456.pdf
Klami_ProbAISchool_2_1647724997596.pdf
bayesian_methods_for_rank_and_preference_data_1647724997994.pdf
elbo_evolution_1647724998609.pdf
elbo_evolution_with_1_samples_1647724998639.pdf
slides-L3_1647724998748.pdf
value_of_information_1647724998905.pdf
combining_model_and_parameter_uncertainty_in_bnns_1647724999078.pdf
slides-deep-generative-models-thomas-lucas_1647725000074.pdf
dlvm_missing_1647725001605.pdf
BayesianSparsificationTutorial_1647725002235.pdf
dgm_thomas_lucas_1647725003000.pdf
dgm_thomas_lucas_1650382583776.pdf
BayesianSparsificationTutorial_1650382582665.pdf
dlvm_missing_1650382581940.pdf
slides-deep-generative-models-thomas-lucas_1650382580628.pdf
slides-L3_1650382579063.pdf
value_of_information_1650382579280.pdf
combining_model_and_parameter_uncertainty_in_bnns_1650382579477.pdf
bayesian_methods_for_rank_and_preference_data_1650382577673.pdf
elbo_evolution_1650382578435.pdf
elbo_evolution_with_1_samples_1650382578529.pdf
variational_inference_and_optimization_1_1650382576871.pdf
Klami_ProbAISchool_2_1650382577081.pdf
introduction_to_probabilistic_modelling_1650382576018.pdf
slides-L2_1650382576531.pdf
evolving_deep_neural_networks_1650382576688.pdf
slides-L1_1650382574823.pdf

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