manuelhaussmann

vgpmil

Variational Bayesian Multiple Instance Learning with Gaussian Processes

ral

Deep Active Learning with Adaptive Acquisition.

vbp

bedl

Bayesian Evidential Deep Learning with PAC Regularization

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

lvm_singlearm

Estimating treatment effects from single-arm trials via latent-variable modeling

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

ai-deadlines

:alarm_clock: AI conference deadline countdowns

aistats2024

May 2 - May 4, Valencia, Spain

BDA_course_Aalto

Bayesian Data Analysis course at Aalto

bnsde

Reference Implementation for Bayesian Neural SDEs

bpnpac

Bayesian Prior Networks with PAC Training

Conference-Acceptance-Rate

Acceptance rates for the major AI conferences

cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

gpytorch

A highly efficient implementation of Gaussian Processes in PyTorch

handsonscicomp

Hands on scientific computing program

inferno

A utility library around PyTorch

manuelhaussmann.github.io

Neural-Process-Family

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

papis

Powerful and highly extensible command-line based document and bibliography manager.

pymc-examples

Examples of PyMC models, including a library of Jupyter notebooks.

pyro

Deep universal probabilistic programming with Python and PyTorch

references

Feel free to clone and add interesting machine learning papers/references in the appropriate category.

rethinking

Statistical Rethinking course and book package

scicomp-docs

Aalto scientific computing guide: former Triton user guide + more info

slides

Slides from meetup presentations.

tikz-bayesnet

TikZ library for drawing Bayesian networks, graphical models and (directed) factor graphs in LaTeX.

united-against-covid.github.io