5 citations · 7 across the 2 of their papers we have counts for
2 papers
quant-ph2025★ 2 cited
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks.…
cs.LG2023★ 5 cited
Criticality versus uniformity in deep neural networks
Aleksandar Bukva, Jurriaan de Gier, Kevin T. Grosvenor +3
Deep feedforward networks initialized along the edge of chaos exhibit exponentially superior training ability as quantified by maximum trainable depth. In this work, we explore the…