A game theoretic approach to explain the output of any machine learning model.

A PyTorch implementation of an Adaptive Memory Multi-Batch L-BFGS

A PyTorch implementation of L-BFGS.

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

A Python package for GPU-accelerated estimation of mixed logit models.

[HELP REQUESTED] Generalized Additive Models in Python

Simulate trajectories, drive cycles and state of charge for battery electric vehicles.

The assignment of a transportation course for my first year as a graduate student, solved the bilevel programming problem using GA algorithm and T-F algorithm.

Program for obtaining the user equilibrium solution with Frank-Wolfe Algorithm in urban traffic assignment

Inverse Stochastic User Equilibrium with LOGIT assignment

Trip generation code of the city-scale synthetic individual-level vehicle trip data.

SIGnificance TESTing Violations: an end-to-end toolkit for evaluating neural networks.

Enabling easy statistical significance testing for deep neural networks.

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Fuzzy String Matching in Python

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

Library to solve Traveling Salesperson Problems with pure Python code

Implementation of SFIT method of feature significance and importance for machine learning models

Some Jupyter Notebooks exploring the methods developed in Giesecke and Horel's paper "Towards Explainable AI: Significance Tests for Neural Networks".