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
CoMoGAN: continuous model-guided image-to-image translation. CVPR 2021 oral.

CoMoGAN: Continuous Model-guided Image-to-Image Translation Official repository. Paper CoMoGAN: continuous model-guided image-to-image translation [ar

166 Dec 31, 2022
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

HV-plane reconstruction from a single 360 image Code for our paper in CVPR 2021: Indoor Panorama Planar 3D Reconstruction via Divide and Conquer (pape

sunset 36 Jan 03, 2023
Byzantine-robust decentralized learning via self-centered clipping

Byzantine-robust decentralized learning via self-centered clipping In this paper, we study the challenging task of Byzantine-robust decentralized trai

EPFL Machine Learning and Optimization Laboratory 4 Aug 27, 2022
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks

Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayes

Intel Labs 210 Jan 04, 2023
Yolov5-lite - Minimal PyTorch implementation of YOLOv5

Yolov5-Lite: Minimal YOLOv5 + Deep Sort Overview This repo is a shortened versio

Kadir Nar 57 Nov 28, 2022
Segmentation models with pretrained backbones. PyTorch.

Python library with Neural Networks for Image Segmentation based on PyTorch. The main features of this library are: High level API (just two lines to

Pavel Yakubovskiy 6.6k Jan 06, 2023
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

Benedek Rozemberczki 251 Nov 30, 2022
Loopy belief propagation for factor graphs on discrete variables, in JAX!

PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in JAX.

Vicarious 62 Dec 23, 2022
Dynamic View Synthesis from Dynamic Monocular Video

Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus

Chen Gao 139 Dec 28, 2022
Reaction SMILES-AA mapping via language modelling

rxn-aa-mapper Reactions SMILES-AA sequence mapping setup conda env create -f conda.yml conda activate rxn_aa_mapper In the following we consider on ex

16 Dec 13, 2022
State-of-the-art language models can match human performance on many tasks

Status: Archive (code is provided as-is, no updates expected) Grade School Math [Blog Post] [Paper] State-of-the-art language models can match human p

OpenAI 259 Jan 08, 2023
Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

Marc Lelarge 36 Dec 12, 2022
CATE: Computation-aware Neural Architecture Encoding with Transformers

CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans

16 Dec 27, 2022
Tianshou - An elegant PyTorch deep reinforcement learning library.

Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Unlike existing reinforcement learning libraries, which are mainly based on

Tsinghua Machine Learning Group 5.5k Jan 05, 2023
PyTorch implementation of adversarial patch

adversarial-patch PyTorch implementation of adversarial patch This is an implementation of the Adversarial Patch paper. Not official and likely to hav

Jamie Hayes 172 Nov 29, 2022
AdvStyle - Official PyTorch Implementation

AdvStyle - Official PyTorch Implementation Paper | Supp Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes. Huiting Ya

Beryl 37 Oct 21, 2022
face2comics by Sxela (Alex Spirin) - face2comics datasets

This is a paired face to comics dataset, which can be used to train pix2pix or similar networks.

Alex 164 Nov 13, 2022
toroidal - a lightweight transformer library for PyTorch

toroidal - a lightweight transformer library for PyTorch Toroidal transformers are of smaller size and lower weight than the more common E-I types. Th

MathInf GmbH 64 Jan 07, 2023
Implementation of 'X-Linear Attention Networks for Image Captioning' [CVPR 2020]

Introduction This repository is for X-Linear Attention Networks for Image Captioning (CVPR 2020). The original paper can be found here. Please cite wi

JDAI-CV 240 Dec 17, 2022
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022