angeloziletti
A data & machine learning scientist that loves to creatively solve complex problems via deep learning, unsupervised learning, and natural language processing
@bayer-science-for-a-better-lifeBerlin, Germany
angeloziletti's Stars
flairNLP/flair
A very simple framework for state-of-the-art Natural Language Processing (NLP)
learning-zone/website-templates
150+ HTML5 Website Templates
scikit-learn-contrib/category_encoders
A library of sklearn compatible categorical variable encoders
jmportilla/Complete-Python-Bootcamp
Lectures for Udemy - Complete Python Bootcamp Course
JasperSnoek/spearmint
Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012
yaringal/ConcreteDropout
Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832
rouyang2017/SISSO
A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.
Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks
Active Learning on Image Data using Bayesian ConvNets
DPBayes/twinify
A software package for privacy-preserving generation of a synthetic twin to a given sensitive data set.
bayer-science-for-a-better-life/data2text-bioleaflets
Biomedical Data-to-Text Generation via Fine-Tuning Transformers
FXIhub/condor
Condor: a simulation tool for flash X-ray imaging
dwadden/scifact-open
Data and code for the SciFact-Open task
DPBayes/d3p
An implementation of the differentially private variational inference algorithm for NumPyro.
Bayer-Group/xtars-naacl2022
Zero/few-shot learning for classification with very large label sets and long-tailed distribution of labels in data points
AilabUdineGit/ontology-pretraining-code
angeloziletti/data2text-bioleaflets
Biomedical Data-to-Text Generation via Fine-Tuning Transformers
JohannesHoehne/contrastive-reconstruction
Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.