We propose a framework to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and reinforcement learning (RL). We formulate the scheduling process of JSSP as a sequential decision-making problem with graph representation of the state to consider the structure of ...
(GPUs) are mainly used. GPU occupancy–the average ratio of active warps to maximum supported warps on all streaming multiprocessors–is an essential indicator of how well GPUs are utilized. Predicting the GPU occupancy of deep learning models is critical for boosting ...
(social networks) for the development of examples and tests (in particular, Cora dataset for examples with the analysis of citations of social media messages), and also an example of a dataset for industrial application in terms of job search SfeduDataset and a special dataset for loading ...
The flexible job shop scheduling problem with lot streaming (FJSPLS) has gained considerable attention due to its potential to significantly reduce manufacturing completion time. FJSPLS couples three important sub-problems: operation sequencing, machine selection, and lot splitting. Deep reinforcement learn...
Scaling Graph Neural Networks Looking forward, GNNs need to scale in all dimensions. Organizations that don’t already maintain graph databases need tools to ease the job of creating these complex data structures. Those who use graph databases know they’re growing in some cases to have thousands...
Due to the unique sparsity of graph data (all pairs of nodes in the graph are connected by only a small number of edges), training directly using general-purpose deep learning frameworks such as TensorFlow and PyTorch tends to perform poorly. If a worker wants to do a good job, he must...
Now that we have quantified the over smoothing issue, you may think that our job is terminated and that it’s enough to add this metric as a regulation term in our loss objective. The problem remaining is that computing those metrics (mentioned above) at each iteration of our traini...
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling 任务:GNN应用在物联网领域 单位:南加州大学 创新点:传感器网络、可穿戴设备和物联网(IoT)设备产生的大量数据突出表明,由于需要边缘计算和许可(数据访问)问题,需要利用分散数据的时空结构的高级建模技术。尽管联邦学习(FL)已经成为一种不需...
I use graph neural networks in my day-to-day job, and I have wasted many days due to the lack of a decent network visualisation tool when trying to explain and review the outputs of a newly trained…
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 a graph that are well suited for some downstream tas...