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Found 10 papers, 4 papers with code
Learning from Pattern Completion: Self-supervised Controllable Generation
Inspired by the neural mechanisms that may contribute to the brain's associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework.
Optimizing Placement and Power Allocation in Reconfigurable Intelligent Sensing Surfaces for Enhanced Sensing and Communication Performance
In this letter, we investigate the design of multiple reconfigurable intelligent sensing surfaces (RISSs) that enhance both communication and sensing tasks.
Grand canonical generative diffusion model for crystalline phases and grain boundaries
The diffusion model has emerged as a powerful tool for generating atomic structures for materials science.
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks
In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning.
Overview: Computer vision and machine learning for microstructural characterization and analysis
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties.
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression and achieved many promising results.
A Deep Image Compression Framework for Face Recognition
In this paper, we propose a deep convolutional autoencoder compression network for face recognition tasks.
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively.
