Backend code to use MCPI's python API to make infinite worlds with custom generation

Last update: Oct 04, 2022

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Deep Learning inf-mcpi

Overview

inf-mcpi

Backend code to use MCPI's python API to make infinite worlds with custom generation

Does not save player-placed blocks!

Generation is still slow, but it does work and can be changed

Documentation to make custom generation is inside the file.

Demo requires perlin-noise to be installed.

Owner

GitHub Repository

Cockpit is a visual and statistical debugger specifically designed for deep learning.

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Backend code to use MCPI's python API to make infinite worlds with custom generation

Related tags

Overview

inf-mcpi

Owner

Cockpit is a visual and statistical debugger specifically designed for deep learning.

Learning where to learn - Gradient sparsity in meta and continual learning

Code for 'Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning' (AAAI 2022)

The Body Part Regression (BPR) model translates the anatomy in a radiologic volume into a machine-interpretable form.

Pre-Trained Image Processing Transformer (IPT)

Compressed Video Action Recognition

Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

3D-Reconstruction 基于深度学习方法的单目多视图三维重建

Code for Paper: Self-supervised Learning of Motion Capture

High-performance moving least squares material point method (MLS-MPM) solver.

The official repo of the CVPR2021 oral paper: Representative Batch Normalization with Feature Calibration

This repository is for the preprint "A generative nonparametric Bayesian model for whole genomes"

EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"

A stock generator that assess a list of stocks and returns the best stocks for investing and money allocations based on users choices of volatility, duration and number of stocks

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Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

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