Metric Entropy Limits on Recurrent Neural Network Learning of Linear Dynamical Systems

no code implementations • 6 May 2021 • Clemens Hutter, Recep Gül, Helmut Bölcskei

One of the most influential results in neural network theory is the universal approximation theorem [1, 2, 3] which states that continuous functions can be approximated to within arbitrary accuracy by single-hidden-layer feedforward neural networks.

Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors

1 code implementation • 27 Nov 2020 • Clemens Hutter, Moritz von Stosch, Mariano Nicolas Cruz Bournazou, Alessandro Butté

Could we exploit data of already developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed.

Transfer Learning