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Found 3 papers, 1 papers with code
Active Learning Approach to Optimization of Experimental Control
In this work we present a general machine learning based scheme to optimize experimental control.
FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution
Most current image super-resolution (SR) methods based on convolutional neural networks (CNNs) use residual learning in network structural design, which favors to effective back propagation and hence improves SR performance by increasing model scale.
Visualizing Neural Network Developing Perturbation Theory
In this letter, motivated by the question that whether the empirical fitting of data by neural network can yield the same structure of physical laws, we apply the neural network to a simple quantum mechanical two-body scattering problem with short-range potentials, which by itself also plays an important role in many branches of physics.
