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rafaelmri's Projects

engrafo icon engrafo

Convert LaTeX documents into beautiful responsive web pages using LaTeXML.

esm icon esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

faceswap icon faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

faceswap-gan icon faceswap-gan

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

fastgeneralizedsuffixarrays icon fastgeneralizedsuffixarrays

A generalized Karkkainen-Sanders implementation in Python for building suffix arrays and trees for many strings in O(n)

firmitas icon firmitas

A cryptocurrency market conditions simulator for testing algorithmic interest rate, quantitative easing and block reward changes

flagmatic icon flagmatic

A package for doing flag algebra computations with the Sage mathematics system. Written in Python and Cython.

fn.py icon fn.py

Functional programming in Python: implementation of missing features to enjoy FP

friedman icon friedman

code for friedman monetary policy in a CK coin

gandissect icon gandissect

Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/

garnet icon garnet

Automatic market maker on Stellar Network

gh-decodes icon gh-decodes

a platform agnostic generative design library for 3d designers

gin-config icon gin-config

Gin provides a lightweight configuration framework for Python

giotto-tda icon giotto-tda

A high-performance topological machine learning toolbox in Python

gitoolbox icon gitoolbox

Automatically exported from code.google.com/p/gitoolbox

golem icon golem

Golem is creating a global market for computing power.

gpt-2 icon gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

gpt2sqa icon gpt2sqa

Fine-tuning GPT-2 Small for Question Answering

gpu-sfft icon gpu-sfft

The implementation of the GPU-SFFT algorithm

graphflow icon graphflow

Deep Learning framework in C++/CUDA that supports symbolic/automatic differentiation, dynamic computation graphs, tensor/matrix operations accelerated by GPU and implementations of various state-of-the-art graph neural networks and other Machine Learning models including Covariant Compositional Networks For Learning Graphs [Risi et al]

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