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cgan icon cgan

这是利用条件GAN(CGAN)实现生成MNIST图像的代码

convlstm icon convlstm

An Open Source Machine Learning Framework for Everyone

deepmind-research icon deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

dgmr-pseudo- icon dgmr-pseudo-

Skilful precipitation nowcasting using deep generative models of radar

dgmr-pytorch icon dgmr-pytorch

An implementation of Deep Generative Model of Radars from DeepMind in PyTorch

e3d_lstm icon e3d_lstm

e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond

fourcastnet icon fourcastnet

Initial public release of code, data, and model weights for FourCastNet

gnnwr icon gnnwr

A PyTorch implementation of the Geographically Neural Network Weighted Regression (GNNWR)

gwann icon gwann

Geographically Weighted Artificial Neural Network

keras-gan icon keras-gan

Keras implementations of Generative Adversarial Networks.

metnet icon metnet

PyTorch Implementation of Google Research's MetNet and MetNet-2

mgwr-python icon mgwr-python

Multiscale Geographically Weighted Regression (MGWR)

opencastkit icon opencastkit

The open-source solutions of FourCastNet and GraphCast

predrnn icon predrnn

Official implementation for NIPS'17 paper: PredRNN: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal LSTMs.

predrnn- icon predrnn-

Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)

pysteps icon pysteps

Python framework for short-term ensemble prediction systems.

spade icon spade

Semantic Image Synthesis with SPADE

srgcnn icon srgcnn

Spatial regression graph convolutional neural networks: Spatial regression analysis conducted in the manner of graph convolutional neural network. Two versions of SRGCNN model are provided in the initial post: a) global regression model (SRGCNN) and b) geographically weighted regression model (SRGCNN-GW)

transformer icon transformer

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

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