rubysidekiqbackground-jobsdependency-graphdirected-graphsidekiq-superworkersidekiq-monitor UpdatedOct 4, 2017 Ruby rkirsling/modallogic Star361 Code Issues Pull requests Modal Logic Playground semanticsdirected-graphmodal-logic UpdatedFeb 1, 2024
以一个graph的邻接表为例，如下图所示:Graph Neural Networks 通过上面的描述，graph可以通过置换不变的...
an efficient reinforcement learning technique that uses a Graph Neural Network (GNN) model to combine all submitted tasks graphs into a single graph to simplify the representation of the states within the environment and efficiently make a parallel application for processing of the submitted jobs. Bes...
Depending on your system, you might also need to tune the mini-batch and/or block size so as to reduce/increase the memory requirement for training jobs. Details on this are available here and here. Using the above parameters, we can run a GraphINVENT training job by running the cell ...
Create hyperparameter tuning jobs to train the model. Deploy the endpoint of the best tuning job and make predictions with the baseline model. Train the Graph Neural Network using the DGL with HPO Graph Neural Networks work by learning representation for nodes or edges of...
Redis houses a queue of all pending analysis jobs. There are two types of jobs in DeepMAPS: The stateful jobs are handled by the Plumber R package to provide real-time interactive analysis; and the stateless jobs, such as CPU-bound bioinformatics pipelines and GPU training tasks that could ...
Added a reinforcement learning framework to allow for fine-tuning models. Fine-tuning jobs can now be run using the --job-type "fine-tune" flag. An example submission script for fine-tuning jobs was added (submit-fine-tuning.py), and the old example submission script was renamed (submit....
A Gentle Introduction to Graph Neural Networks https://www.youtube.com/watch?v=GXhBEj1ZtE8 http...
Runtime Performance Prediction for Deep Learning Models with Graph Neural Network Yanjie Gao1, Xianyu Gu1, 3∗, Hongyu Zhang2, Haoxiang Lin1†, Mao Yang1 1Microsoft Research, Beijing, China Email: {yanjga, haoxlin, maoyang}@microsoft.com 2Chongqing University, Chongqing, China Email: ...
In this work, we propose One Class Graph Neural Network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of Graph Neural Networks along with the classical one-class objective. Compared with other baselines, ...