Pinned Repositories
2016
AD3
Alternating Directions Dual Decomposition
alpha-zero-general
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4
alphafold
Open source code for AlphaFold.
angrybirds
AI Final Project
apricot
apricot implements submodular selection for the purpose of selecting subsets of massive data sets to train machine learning models quickly.
arXausality
A every-so-often-updated collection of every causality + machine learning paper submitted to arXiv in the recent past.
arxiv-sanity-preserver
Web interface for browsing, search and filtering recent arxiv submissions
opveclib
The Operator Vectorization Library, or OVL, is a python productivity library for defining high performance custom operators for the TensorFlow platform.
platoon
Multi-GPU mini-framework for Theano
fedorajzf's Repositories
fedorajzf/alphafold
Open source code for AlphaFold.
fedorajzf/blackbox-backprop
Torch modules that wrap blackbox combinatorial solvers according to the method presented in "Differentiating Blackbox Combinatorial Solvers"
fedorajzf/causal-inference-tutorial
Repository with code and slides for a tutorial on causal inference.
fedorajzf/cfcausal
R package cfcausal
fedorajzf/conformal
Tools for conformal inference in regression
fedorajzf/data-driven-pdes
fedorajzf/deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
fedorajzf/DiffGeoOps
This repository contains a Python implementation of the paper "Discrete Differential-Geometry Operators for Triangulated 2-Manifolds" by Meyer et. al. VisMath 2002
fedorajzf/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.
fedorajzf/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.
fedorajzf/graph_nets
Build Graph Nets in Tensorflow
fedorajzf/IntrinsicallyEfficientStableOPE
fedorajzf/kernel_reg
Pytorch implementation of regularization methods for deep networks obtained via kernel methods.
fedorajzf/lp-sparsemap
LP-SparseMAP: Differentiable sparse structured prediction in coarse factor graphs
fedorajzf/minigo
An open-source implementation of the AlphaGoZero algorithm
fedorajzf/models
Models and examples built with TensorFlow
fedorajzf/neural-structural-optimization
Neural reparameterization improves structural optimization
fedorajzf/oracle_cb
Experimentation for oracle based contextual bandit algorithms.
fedorajzf/probml-notebooks
Notebooks for "Probabilistic Machine Learning" book
fedorajzf/py-orthpol
Construct orhogonal polynomials using Python
fedorajzf/pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
fedorajzf/ranking
Learning to Rank in TensorFlow
fedorajzf/RLSeq2Seq
Deep Reinforcement Learning For Sequence to Sequence Models
fedorajzf/Seq2Set
Code for the paper "A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification"
fedorajzf/SGM
Sequence Generation Model for Multi-label Classification (COLING 2018)
fedorajzf/slides
fedorajzf/SurvivalAnalysis
fedorajzf/tutorials-2
fedorajzf/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).
fedorajzf/uq-course
Introduction to Uncertainty Quantification