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aitools

C++ library that contains basic AI data structures and algorithm, in particular decision trees and probabilistic circuits.

blocks

Python script for solving 2D and 3D block puzzles

cm-draughtsboard

A JavaScript draughtsboard component that is rendered in SVG.

conestrip

An implementation of the CONEstrip algorithms using python and z3

data-parallel-cpp

Source code for 'Data Parallel C++: Mastering DPC++ for Programming of Heterogeneous Systems using C++ and SYCL' by James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian (Apress, 2020).

dparser

A Scannerless GLR parser/parser generater.

draughts

A C++ library for international draughts, based on the program Scan. This library has been used in several master thesis projects.

draughtsboard

Javascript draughts board

egdb_intl

Functions to access the kingsrow international draughts endgame database

frameworks

gambatools

A library for formal language education. It contains support for DFAs, NFAs, PDAs, Turing machines, context free grammars and regular expressions.

gefs

Generative Forests in Python

in-time-over-parameterization

[ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

large-scale-sparse-neural-networks

lyra

A simple to use, composable, command line parser for C++ 11 and beyond

mackerel

mcrl2

The Git repository for the mCRL2 toolset.

minimal-cnpy

nerva

C++ library for neural networks with python bindings.

nerva-jax

Implementation of neural networks in JAX

nerva-numpy

Implementation of neural networks in NumPy

nerva-sympy

Implementation of neural networks in SymPy

nerva-tensorflow

Implementation of neural networks in TensorFlow

nerva-torch

Implementation of neural networks in PyTorch

nerva_stub

Stub for the Nerva package

pdn

The official portable draughts notation standard PDN 3.0

pydraughts

A draughts (checkers) library for Python with move generation, PDN reading and writing, engine communication and balloted openings

pypi_example

random_pruning

[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training by Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy

rigl

End-to-end training of sparse deep neural networks with little-to-no performance loss.