This repository maintains the official implementation of the paperLearning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing ImagesbyYe Liu,Huifang Li,Chao Hu,Shuang Luo,Yan Luo, andChang Wen Chen, which has been accepted byTNNLS. ...
Our approach relies on local communication between topologically adjacent agents to reduce communication costs, power consumption and computational complexity. Each agent receives the state observation from its neighbours and aggregates them with its state and action to obtain the final decision-dependent ...
The integration of computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM) systems is significantly enhanced by employing deep learning-based automatic feature recognition (AFR) methods. These methods o
int layersToFreeze: int learningRate: int learningRateScheduler: 'string' maxSize: int minSize: int modelName: 'string' modelSize: 'string' momentum: int multiScale: bool nesterov: bool nmsIouThreshold: int numberOfEpochs: int numberOfWorkers: int optimizer: 'string' randomSeed...
In each iteration, a number of clients first compute their local model updates and upload them to a server, which aggregates the local model updates into a global one to update its maintained global model. Then, the server distributes the global update to each client to conduct a local ...
fix explanation dashboard not showing aggregate feature importances for sparse engineered explanations optimized memory usage of ExplanationClient in azureml-interpret package azureml-train-automl-client Fixed show_output=False to return control to the user when running using spark. 2021-02-...
int layersToFreeze: int learningRate: int learningRateScheduler: 'string' maxSize: int minSize: int modelName: 'string' modelSize: 'string' momentum: int multiScale: bool nesterov: bool nmsIouThreshold: int numberOfEpochs: int numberOfWorkers: int optimizer: 'string' randomSeed: int stepLR...
Recently, multi-agent reinforcement learning (MARL) has been proposed for secondary control [22,23,24,25]. As agents interact with the environment and learn offline by simulating other agents’ strategies to cooperate, they can find the optimal strategy. After training, agents can adapt well to...
The accuracy results of the predicted water body extracted from the segmentation task for the aggregate dataset are shown in Figure 6 and the performance of per-class metrics is illustrated in Figure 7 and Figure 8. Figure 6. A comparison of segmentation metrics for the entire dataset for both...
BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale content type paper | research area Knowledge Bases and Search, research area Methods and Algorithms | conference AAAIPublished year 2024 AuthorsRandy Ardywibowo, Rakesh Sunki, Lucy Kuo, Sankal...