Stars
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
some bravo or inspiring research works on the topic of curriculum learning
A unified framework for Deep Learning Models on tabular data
A Data-Centric library providing a unified interface for state-of-the-art methods for hardness characterisation of data points.
A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.
A scikit-learn compatible library for graph kernels
PyTorch Explain: Interpretable Deep Learning in Python.
Graph Neural Network Library for PyTorch
A highly efficient implementation of Gaussian Processes in PyTorch
A Python package for building Bayesian models with TensorFlow or PyTorch
A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.
Tools for training explainable models using attribution priors.
📚 Parameterize, execute, and analyze notebooks
A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
Pytorch implementation of convolutional neural network visualization techniques
Practice your pandas skills!
Automatic GPU+CPU memory profiling, re-use and memory leaks detection using jupyter/ipython experiment containers
A tensorflow implementation of VAE-GAN. This is the first approach which viewed the discriminator as a loss function to improve.
Keras implementation of the paper "Autoencoding beyond pixels using a learned similarity metric"
General-purpose library for extracting interpretable models from Multi-Agent Reinforcement Learning systems
Code for "Testing Robustness Against Unforeseen Adversaries"
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
📚 Find your next book to read!