Towards Real-World Super-Resolution using Deep Neural Networks

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Towards Real-World Super-Resolution using Deep Neural Networks

Chattez avec Graph Search

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Probing the latent hierarchical structure of data via diffusion models

Self-supervised Change Detection via Cooperative Learning: A Two-Player Model

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Domain shift, domain adaptation, and generalization: A focus on MRI

Partial discharge localization in power transformer tanks using machine learning methods

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Robust NAS under adversarial training: benchmark, theory, and beyond

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Robust machine learning for neuroscientific inference

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Residual-based attention in physics-informed neural networks

Aggregating Spatial and Photometric Context for Photometric Stereo

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Random matrix methods for high-dimensional machine learning models

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