Pretrained Cost Model for Distributed Constraint Optimization Problems

Last update: Aug 28, 2022

Related tags

Deep Learning GAT-PCM

Overview

Pretrained Cost Model for Distributed Constraint Optimization Problems

Requirements

PyTorch 1.9.0
PyTorch Geometric 1.7.1

Directory structure

baselines contains the implementation of all compared baselines
core contains the core data structures to run the simulation
entry contains the entry point of each algorithm
heuristics contains the implementation of GAT-PCM-boosted algorithms
pretrain contains the implementation of pretraining stage

How to run the code

See the command line interface of run_*.py in entry.

Example:

python -um entry.run_dsa -pth problem.xml -c 1000 -p 0.8

Owner

GitHub Repository

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Pretrained Cost Model for Distributed Constraint Optimization Problems

Related tags

Overview

Pretrained Cost Model for Distributed Constraint Optimization Problems

Requirements

Directory structure

How to run the code

Owner

nnFormer: Interleaved Transformer for Volumetric Segmentation

A denoising diffusion probabilistic model synthesises galaxies that are qualitatively and physically indistinguishable from the real thing.

Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.

[SIGGRAPH Asia 2021] Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet)

simple demo codes for Learning to Teach with Dynamic Loss Functions

CCCL: Contrastive Cascade Graph Learning.

Meli Data Challenge 2021 - First Place Solution

A simple Python configuration file operator.

Pytorch implementation of the paper Progressive Growing of Points with Tree-structured Generators (BMVC 2021)

For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

Official Implementation of PCT

GUPNet - Geometry Uncertainty Projection Network for Monocular 3D Object Detection

Relative Uncertainty Learning for Facial Expression Recognition

Official implementation of the NRNS paper: No RL, No Simulation: Learning to Navigate without Navigating

CLIP + VQGAN / PixelDraw

This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

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