Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.

Last update: Jul 20, 2022

Related tags

Overview

Minesweeper-AI

Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.

Requirements

• Python 3.9  
• pygame

Simple step-by-step tutorial

Run runner.py
Press 'Play Game'
Click on 'AI Move' and let the AI do the work :)

Credits

All credits due to the Harvard CS50 team for their work on the game files

Owner

Beckham

Year 1 Undergraduate Student Majoring in Computer Science & Design

GitHub Repository

This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.

MCGC Description This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021. Datasets Results ACM DBLP IMDB Amazon photos Amazon co

31 Nov 14, 2022

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud This repository contains a reference implementation of our Part-Aware Data Augment

62 Jan 03, 2023

Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface Usage TransformMultiStepScheduler import albumentations a

19 Aug 04, 2021

D2Go is a toolkit for efficient deep learning

D2Go D2Go is a production ready software system from FacebookResearch, which supports end-to-end model training and deployment for mobile platforms. W

744 Jan 04, 2023

ChebLieNet, a spectral graph neural network turned equivariant by Riemannian geometry on Lie groups.

ChebLieNet: Invariant spectral graph NNs turned equivariant by Riemannian geometry on Lie groups Hugo Aguettaz, Erik J. Bekkers, Michaël Defferrard We

12 Dec 10, 2022

Code for: https://berkeleyautomation.github.io/bags/

DeformableRavens Code for the paper Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks. Here is the

121 Dec 30, 2022

Gradient Step Denoiser for convergent Plug-and-Play

Source code for the paper "Gradient Step Denoiser for convergent Plug-and-Play"

11 Sep 17, 2022

Implementation of the ICCV'21 paper Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases [Papers 1, 2][Project page] [Video] The implementation of the papers Temporal

56 Nov 21, 2022

Bravia core script for python

Bravia-Core-Script You need to have a mandatory account If this L3 does not work, try another L3. enjoy

5 Dec 26, 2021

Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

EPSR (Enhanced Perceptual Super-resolution Network) paper This repo provides the test code, pretrained models, and results on benchmark datasets of ou

78 Nov 19, 2022

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

258 Dec 29, 2022

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision This is the repository for our Paper/Contribution to the WI2022 in Nürnber

6 Jan 17, 2022

Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"

Boundary-Guided-Context-Aggregation Boundary Guided Context Aggregation for Semantic Segmentation Haoxiang Ma, Hongyu Yang, Di Huang In BMVC'2021 Pape

31 Jan 08, 2023

SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise

Automating reinforcement learning architecture design for code optimization. Che

113 Nov 28, 2022

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

141 Jan 07, 2023

Created as part of CS50 AI's coursework. This AI makes use of knowledge entailment to calculate the best probabilities to win Minesweeper.

Related tags

Overview

Minesweeper-AI

Requirements

Simple step-by-step tutorial

Credits

Owner

Beckham

This is the code of "Multi-view Contrastive Graph Clustering" in NeurlPS 2021.

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Image data augmentation scheduler for albumentations transforms

D2Go is a toolkit for efficient deep learning

ChebLieNet, a spectral graph neural network turned equivariant by Riemannian geometry on Lie groups.

Code for: https://berkeleyautomation.github.io/bags/

Gradient Step Denoiser for convergent Plug-and-Play

Implementation of the ICCV'21 paper Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

Bravia core script for python

Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

Code for BMVC2021 paper "Boundary Guided Context Aggregation for Semantic Segmentation"

SuperSonic, a new open-source framework to allow compiler developers to integrate RL into compilers easily, regardless of their RL expertise

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

Install alphafold on the local machine, get out of docker.

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

🐦 Quickly annotate data from the comfort of your Jupyter notebook

Source code for the NeurIPS 2021 paper "On the Second-order Convergence Properties of Random Search Methods"