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Found 5 papers, 2 papers with code
Generalization Through The Lens Of Leave-One-Out Error
Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited.
The power of quantum neural networks
We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.
Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state.
Quantum Physics
Topological Map Extraction from Overhead Images
We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly.
Generator Reversal
We consider the problem of training generative models with deep neural networks as generators, i. e. to map latent codes to data points.
