Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

Last update: Nov 17, 2021

Related tags

Overview

CMKN

Implementation of the convolutional motif kernel network (CMKN) introduced in Ditz et al., "Convolutional Motif Kernel Network", 2021.

Testing

You can run the unit-tests by executing

$ python -m unittest

from the root of the project. The tests include some standard problems of pulse propagation in nonlinear media. During the tests an interactive plotter demonstrating the integration results will be shown. Unfortunately, at the moment it is not possible to disable it, so running tests in a headless setup is not straightforward.

Documentation

The documentation is written with sphinx. You can build it by running

$ cd doc && make html

from the root of the project. The entry point for the documentation will be placed in doc/_build/html/index.html which you can open with a browser of your choice.

You might also like...

Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

Intention Adaptive Graph Neural Network (IAGNN) This is the official repository of paper Intention Adaptive Graph Neural Network for Category-Aware Se

9 Nov 22, 2022

Implementation of character based convolutional neural network

Character Based CNN This repo contains a PyTorch implementation of a character-level convolutional neural network for text classification. The model a

248 Nov 21, 2022

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

251 Nov 30, 2022

A PyTorch implementation of the Relational Graph Convolutional Network (RGCN).

Torch-RGCN Torch-RGCN is a PyTorch implementation of the RGCN, originally proposed by Schlichtkrull et al. in Modeling Relational Data with Graph Conv

66 Nov 30, 2022

An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Retina Blood Vessels Segmentation This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional

23 Aug 20, 2022

Pytorch implementation of Cut-Thumbnail in the paper Cut-Thumbnail:A Novel Data Augmentation for Convolutional Neural Network.

Cut-Thumbnail (Accepted at ACM MULTIMEDIA 2021) Tianshu Xie, Xuan Cheng, Xiaomin Wang, Minghui Liu, Jiali Deng, Tao Zhou, Ming Liu This is the officia

3 Apr 12, 2022

Official implementation of the paper "Steganographer Detection via a Similarity Accumulation Graph Convolutional Network"

SAGCN - Official PyTorch Implementation | Paper | Project Page This is the official implementation of the paper "Steganographer detection via a simila

1 Nov 26, 2021

An implementation of quantum convolutional neural network with MindQuantum. Huawei, classifying MNIST dataset

关于实现的一点说明山东大学 2020级苏博南 www.subonan.com 文件说明 tools.py 这里面主要有两个函数： resize(a, lenb) 这其实是我找同学写的一个小算法hhh。给出一个$28\times 28$的方阵a，返回一个$lenb\times lenb$的方阵。因

2 Aug 29, 2022

Code for Mesh Convolution Using a Learned Kernel Basis

Mesh Convolution This repository contains the implementation (in PyTorch) of the paper FULLY CONVOLUTIONAL MESH AUTOENCODER USING EFFICIENT SPATIALLY

35 Jan 3, 2023

Releases(v0.1)

v0.1(Nov 15, 2021)

This release is the first version of convolutional motif kernel networks. The code includes PyTorch objects to construct a CMKN model and handle input data. Furthermore, methods to analyze and interpret a trained CMKN model are included as well as routines to prepare HIVdb's data for experiments. A first documentation of the project can be build with sphinx.
Source code(tar.gz)
Source code(zip)

Owner

GitHub Repository

An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

141 Jan 07, 2023

EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

EncT5 (Unofficial) Pytorch Implementation of EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks About Finetune T5 model for classification & r

34 Jan 01, 2023

Collection of generative models in Pytorch version.

pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r

2.4k Dec 31, 2022

NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

#NeuralTalk Warning: Deprecated. Hi there, this code is now quite old and inefficient, and now deprecated. I am leaving it on Github for educational p

5.3k Jan 07, 2023

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

Conditional Smiles! (SmileCVAE) About Implementation of AE, VAE and CVAE. Trained CVAE on faces from UTKFace Dataset. Using an encoding of the Smile-s

3 Jan 09, 2022

Phy-Q: A Benchmark for Physical Reasoning

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

29 Dec 19, 2022

Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge Introduction SentiLARE is a sentiment-aware pre-trained language

74 Dec 30, 2022

Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021

Frequency Bias of Generative Models Generator Testbed Discriminator Testbed This repository contains official code for the paper On the Frequency Bias

35 Nov 01, 2022

Pytorch implementation of Learning with Opponent-Learning Awareness

Pytorch implementation of Learning with Opponent-Learning Awareness using DiCE

82 Sep 15, 2022

Python and C++ implementation of "MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation". Accepted at LXCV @ CVPR 2021.

MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation This is a PyTorch and LibTorch implementation of MarkerPose: a

47 Nov 18, 2022

《Geo Word Clouds》paper implementation

2 Jan 28, 2022

This is my codes that can visualize the psnr image in testing videos.

CVPR2018-Baseline-PSNRplot This is my codes that can visualize the psnr image in testing videos. Future Frame Prediction for Anomaly Detection – A New

12 May 29, 2021

2021搜狐校园文本匹配算法大赛分比我们低的都是帅哥队

sohu_text_matching 2021搜狐校园文本匹配算法大赛Top2：分比我们低的都是帅哥队本repo包含了本次大赛决赛环节提交的代码文件及答辩PPT，提交的模型文件可在百度网盘获取（链接：https://pan.baidu.com/s/1T9FtwiGFZhuC8qqwXKZSNA ，

43 Oct 01, 2022

PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules

Dynamic Routing Between Capsules - PyTorch implementation PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules from Sara Sabour,

475 Dec 24, 2022

Automatic differentiation with weighted finite-state transducers.

GTN: Automatic Differentiation with WFSTs Quickstart | Installation | Documentation What is GTN? GTN is a framework for automatic differentiation with

100 Dec 29, 2022

Machine Learning with JAX Tutorials

The purpose of this repo is to make it easy to get started with JAX. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I fou

372 Dec 28, 2022

Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated Learning

Federated_Learning This repo provides a federated learning framework that allows to carry out backdoor attacks under varying conditions. This is a ker

0 Nov 30, 2021

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

DeepCAD This repository provides source code for our paper: DeepCAD: A Deep Generative Network for Computer-Aided Design Models Rundi Wu, Chang Xiao,

85 Dec 31, 2022

An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

Agar.io_Q-Learning_AI An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available act

1 Jun 09, 2022

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

17 Jun 10, 2022