A library that implements fairness-aware machine learning algorithms
Interpretability and explainability of data and machine learning models
A Large-Scale PyTorch Language Model trained on the 1-Billion Word (LM1B) / (GBW) dataset
The first alphamev MEV competition with best AUC (0.9893) and MSE (0.0982)
easyopt is a super simple yet super powerful optuna-based Hyperparameters Optimization Framework that requires no coding
A Tensorflow based library for Time Series Modelling with Gaussian Processes
Neural machine translation between the writings of Shakespeare and modern English using TensorFlow
Tensorforce: a TensorFlow library for applied reinforcement learning
Tree-Structured Long Short-Term Memory Networks in PyTorch
OptNet: Differentiable Optimization as a Layer in Neural Networks
A pytorch implementation of auto-punctuation learned character by character
Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge
A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python
MACEst is a confidence estimator that can be used alongside any model (regression or classification) which uses previously seen data (i.e. any supervised learning model) to produce a point prediction.
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.