Tw1stcc

MAML-Pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

building_transfer

Experiments in transfer learning for naive fault and degradation detection in time dynamic systems

MAVBench

Simulator + benchmark suite for Micro Aerial Vehicle design.

Matlab-Machine

LSSVM

Python implementation of Least Squares Support Vector Machine for classification on CPU (NumPy) and GPU (PyTorch).

mlxtend

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Efficient-Apriori

An efficient Python implementation of the Apriori algorithm.

Air-Quality-Prediction

2021年研究生数学建模竞赛B题，全国二等奖，空气质量预报二次建模，时间序列数据分析与回归预测。Time Series Prediction&Air Quality Prediction.

imd

Code for MSID, a Multi-Scale Intrinsic Distance for comparing generative models, studying neural networks, and more!

CausalityEventExtraction

Causality event extraction demo project including casual patterns and experiment on large scale corpus. 基于因果关系知识库的因果事件图谱实验项目，本项目罗列了因果显式表达的几种模式，基于这种模式和大规模语料，再经过融合等操作，可形成因果事件图谱。

causica

DomainBed

DomainBed is a suite to test domain generalization algorithms

TCDF

Temporal Causal Discovery Framework (PyTorch): discovering causal relationships between time series

annotated_deep_learning_paper_implementations

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

fitter

Fit data to many distributions

hypertools

A Python toolbox for gaining geometric insights into high-dimensional data

datahike

A fast, immutable, distributed & compositional Datalog engine for everyone.

FlowDelta

FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

ladder-latent-data-distribution-modelling

In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i.e. its topology and structural properties. We propose LaDDer to achieve accurate modelling of the latent data distribution in a variational autoencoder framework and to facilitate better representation learning. The central idea of LaDDer is a meta-embedding concept, which uses multiple VAE models to learn an embedding of the embeddings, forming a ladder of encodings. We use a non-parametric mixture as the hyper prior for the innermost VAE and learn all the parameters in a unified variational framework. From extensive experiments, we show that our LaDDer model is able to accurately estimate complex latent distribution and results in improvement in the representation quality.

pytorch-CycleGAN-and-pix2pix

Image-to-Image Translation in PyTorch

PyTorch_Tutorial

《Pytorch模型训练实用教程》中配套代码