Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Related tags

Deep LearningDGCN
Overview

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Setup

This implementation is based on PyTorch >= 1.0.0. Small dataset (including Cora, Citeseer, and Pubmed) are located in the data folder.

Usage

Please use the jupyter notebook files localed in the ./jupyter_notebooks. Please copy the file you want to run to the root folder, i.e., ./DGCN/CODE_WANT_TO_RUN.ipynb, then directly run it using jupyter notebook.

Owner
Weilin Cong
Graduate student at Pennsylvania State University.
Weilin Cong
This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans This repository contains the implementation of the pap

Photogrammetry & Robotics Bonn 40 Dec 01, 2022
AgML is a comprehensive library for agricultural machine learning

AgML is a comprehensive library for agricultural machine learning. Currently, AgML provides access to a wealth of public agricultural datasets for common agricultural deep learning tasks.

Plant AI and Biophysics Lab 1 Jul 07, 2022
AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition.

AnimalAI 3 AAI supports interdisciplinary research to help better understand human, animal, and artificial cognition. It aims to support AI research t

Matthew Crosby 58 Dec 12, 2022
This is the implementation of the paper "Self-supervised Outdoor Scene Relighting"

Self-supervised Outdoor Scene Relighting This is the implementation of the paper "Self-supervised Outdoor Scene Relighting". The model is implemented

Ye Yu 24 Dec 17, 2022
Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch

Jiangjingwen 13 Jan 06, 2023
This repository contains code released by Google Research.

This repository contains code released by Google Research.

Google Research 26.6k Dec 31, 2022
This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework

neon_course This repository contains several jupyter notebooks to help users learn to use neon, our deep learning framework. For more information, see

Nervana 92 Jan 03, 2023
Transport Mode detection - can detect the mode of transport with the help of features such as acceeration,jerk etc

title emoji colorFrom colorTo sdk app_file pinned Transport_Mode_Detector 🚀 purple yellow gradio app.py false Configuration title: string Display tit

Nishant Rajadhyaksha 3 Jan 16, 2022
Fast and accurate optimisation for registration with little learningconvexadam

convexAdam Learn2Reg 2021 Submission Fast and accurate optimisation for registration with little learning Excellent results on Learn2Reg 2021 challeng

17 Dec 06, 2022
Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

Child-Tuning Source code for EMNLP 2021 Long paper: Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. 1. Environ

46 Dec 12, 2022
salabim - discrete event simulation in Python

Object oriented discrete event simulation and animation in Python. Includes process control features, resources, queues, monitors. statistical distrib

181 Dec 21, 2022
The official re-implementation of the Neurips 2021 paper, "Targeted Neural Dynamical Modeling".

Targeted Neural Dynamical Modeling Note: This is a re-implementation (in Tensorflow2) of the original TNDM model. We do not plan to further update the

6 Oct 05, 2022
Rede Neural Convolucional feita durante o processo seletivo do LaboratĂłrio de InteligĂȘncia Artificial da FACOM (UFMS)

Primeira_Rede_Neural_Convolucional Rede Neural Convolucional feita durante o processo seletivo do LaboratĂłrio de InteligĂȘncia Artificial da FACOM (UFM

Roney_Felipe 1 Jan 13, 2022
Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

HAWQ: Hessian AWare Quantization HAWQ is an advanced quantization library written for PyTorch. HAWQ enables low-precision and mixed-precision uniform

Zhen Dong 293 Dec 30, 2022
This repository implements variational graph auto encoder by Thomas Kipf.

Variational Graph Auto-encoder in Pytorch This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to

DaehanKim 215 Jan 02, 2023
A comprehensive and up-to-date developer education platform for Urbit.

curriculum A comprehensive and up-to-date developer education platform for Urbit. This project organizes developer capabilities into a hierarchy of co

Sigilante 36 Oct 04, 2022
CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

Fraunhofer SCAI 10 Oct 11, 2022
Ranger deep learning optimizer rewrite to use newest components

Ranger21 - integrating the latest deep learning components into a single optimizer Ranger deep learning optimizer rewrite to use newest components Ran

Less Wright 266 Dec 28, 2022
Simultaneous NMT/MMT framework in PyTorch

This repository includes the codes, the experiment configurations and the scripts to prepare/download data for the Simultaneous Machine Translation wi

<a href=[email protected]"> 37 Sep 29, 2022
TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition This is an implementation of TCPNet. Introduction For video recognition task, a g

Zilin Gao 21 Dec 08, 2022