Code for approximate graph reduction techniques for cardinality-based DSFM, from paper

Overview

SparseCard

Code for approximate graph reduction techniques for cardinality-based DSFM, from paper

"Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components"

Nate Veldt, Austin Benson, Jon Kleinberg. NeurIPS 2021.

The include folder contains implementations for outside code needed for experimental comparisons.

The src folder includes implementations of our main methods.

There is a folder for each of the main experiments (one for image segmentation, one for hypergraph clustering).

For image segmentation experiments

Code for running image segmentation experiments with competing continuous optimization techniques is given in

include/DSFM-with-incidence-relations-v2

In order to reproduce these experiments, see

Run_all_image_exps.m

For hypergraph clustering experiments

You will need to place the stackoverflow-answers dataset in the data folder in order to run experiments

https://www.cs.cornell.edu/~arb/data/stackoverflow-answers/

To reproduce experiments, run

stackoverflow_runall.jl

This will take a long time, as this file runs 4500 individual local clustering experiments.

Owner
Nate Veldt
Assistant Professor, Department of Computer Science and Engineering, Texas A&M University
Nate Veldt
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
Deep Learning for Human Part Discovery in Images - Chainer implementation

Deep Learning for Human Part Discovery in Images - Chainer implementation NOTE: This is not official implementation. Original paper is Deep Learning f

Shintaro Shiba 63 Sep 25, 2022
Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Heterogeneous INteract and aggreGatE (GraphHINGE) This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following w

Jinjiarui 69 Nov 24, 2022
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
Learning to trade under the reinforcement learning framework

Trading Using Q-Learning In this project, I will present an adaptive learning model to trade a single stock under the reinforcement learning framework

Uirá Caiado 470 Nov 28, 2022
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

Bo Wan 32 Dec 23, 2022
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links

LinkBERT: A Knowledgeable Language Model Pretrained with Document Links This repo provides the model, code & data of our paper: LinkBERT: Pretraining

Michihiro Yasunaga 264 Jan 01, 2023
Get started learning C# with C# notebooks powered by .NET Interactive and VS Code.

.NET Interactive Notebooks for C# Welcome to the home of .NET interactive notebooks for C#! How to Install Download the .NET Coding Pack for VS Code f

.NET Platform 425 Dec 25, 2022
PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

0 Mar 01, 2022
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Score-Based Generative Modeling through Stochastic Differential Equations This repo contains a PyTorch implementation for the paper Score-Based Genera

Yang Song 757 Jan 04, 2023
This repo is developed for Strong Baseline For Vehicle Re-Identification in Track 2 Ai-City-2021 Challenges

A STRONG BASELINE FOR VEHICLE RE-IDENTIFICATION This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition Workshop(CVPR

Cybercore Co. Ltd 78 Dec 29, 2022
Ros2-voiceroid2 - ROS2 wrapper package of VOICEROID2

ros2_voiceroid2 ROS2 wrapper package of VOICEROID2 Windows Only Installation Ins

Nkyoku 1 Jan 23, 2022
Omniscient Video Super-Resolution

Omniscient Video Super-Resolution This is the official code of OVSR (Omniscient Video Super-Resolution, ICCV 2021). This work is based on PFNL. Datase

36 Oct 27, 2022
A Python module for the generation and training of an entry-level feedforward neural network.

ff-neural-network A Python module for the generation and training of an entry-level feedforward neural network. This repository serves as a repurposin

Riadh 2 Jan 31, 2022
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving This is the source code for our paper Frequency Domain Image Tran

Mu Cai 52 Dec 23, 2022
Generalized Decision Transformer for Offline Hindsight Information Matching

Generalized Decision Transformer for Offline Hindsight Information Matching [arxiv] If you use this codebase for your research, please cite the paper:

Hiroki Furuta 35 Dec 12, 2022
Generative Adversarial Text-to-Image Synthesis

###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee This is the

Scott Ellison Reed 883 Dec 31, 2022
MoCoPnet - Deformable 3D Convolution for Video Super-Resolution

Deformable 3D Convolution for Video Super-Resolution Pytorch implementation of l

Xinyi Ying 28 Dec 15, 2022
Single/multi view image(s) to voxel reconstruction using a recurrent neural network

3D-R2N2: 3D Recurrent Reconstruction Neural Network This repository contains the source codes for the paper Choy et al., 3D-R2N2: A Unified Approach f

Chris Choy 1.2k Dec 27, 2022
Grow Function: Generate 3D Stacked Bifurcating Double Deep Cellular Automata based organisms which differentiate using a Genetic Algorithm...

Grow Function: A 3D Stacked Bifurcating Double Deep Cellular Automata which differentiates using a Genetic Algorithm... TLDR;High Def Trees that you can mint as NFTs on Solana

Nathaniel Gibson 4 Oct 08, 2022