Popular GitHub Repositories for Zombie Survival Shooter

Survival analysis with PyTorch

A project that uses optical flow and machine learning to detect aimhacking in video clips.

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Source code for the book Core ML Survival Guide

GPstuff - Gaussian process models for Bayesian analysis

A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.

First person shooter with Unity terrain and AI pathfinding (http://gdev.tv/cudgithub)

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Decoding biological age from face photographs using deep learning.

This application aims to early detection of lung cancer to give patients the best chance at recovery and survival using CNN Model.

SALMON: Survival Analysis Learning with Multi-Omics Neural Networks

[ICLR 2025] Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology

Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.

??♂? A Unity based FPS with a Zombie theme using raycasting for shooting and the core mechanics you'd expect in an FPS. Zombies use AI navigation and precise pathfinding to chase down the player. The game also has a HUD interface which is VR compatible.

This data-centric AI repository implements a robust deep learning method (LFBNet) for fully automated tumor segmentation in whole-body [18]F-FDG PET/CT images.

An Artificial Life simulator for Investigating Noogenesis

This repository is a tutorial about survival analysis based on advanced machine learning methods including Random Forest, Gradient Boosting Tree and XGBoost. All of them are implemented in R.

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

Prognostically Relevant Subtypes and Survival Prediction for Breast Cancer Based on Multimodal Genomics Data