fedorajzf's repositories
alphafold
Open source code for AlphaFold.
deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
conformal
Tools for conformal inference in regression
pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
probml-notebooks
Notebooks for "Probabilistic Machine Learning" book
cfcausal
R package cfcausal
uq-course
Introduction to Uncertainty Quantification
DiffGeoOps
This repository contains a Python implementation of the paper "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds" by Meyer et. al. VisMath 2002
uncertainty_estimation_deep_learning
This repository provides the code used to implement the framework to provide deep learning models with total uncertainty estimates as described in "A General Framework for Uncertainty Estimation in Deep Learning" (Loquercio, Segù, Scaramuzza. RA-L 2020).
RLSeq2Seq
Deep Reinforcement Learning For Sequence to Sequence Models
lp-sparsemap
LP-SparseMAP: Differentiable sparse structured prediction in coarse factor graphs
Seq2Set
Code for the paper "A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification"
SGM
Sequence Generation Model for Multi-label Classification (COLING 2018)
blackbox-backprop
Torch modules that wrap blackbox combinatorial solvers according to the method presented in "Differentiating Blackbox Combinatorial Solvers"
neural-structural-optimization
Neural reparameterization improves structural optimization
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
oracle_cb
Experimentation for oracle based contextual bandit algorithms.
py-orthpol
Construct orhogonal polynomials using Python
causal-inference-tutorial
Repository with code and slides for a tutorial on causal inference.
graph_nets
Build Graph Nets in Tensorflow
kernel_reg
Pytorch implementation of regularization methods for deep networks obtained via kernel methods.
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
minigo
An open-source implementation of the AlphaGoZero algorithm
models
Models and examples built with TensorFlow
ranking
Learning to Rank in TensorFlow