27 citations · 27 across the 2 of their papers we have counts for
3 papers
Improving Sample Efficiency with Normalized RBF Kernels
Sebastian Pineda-Arango, David Obando-Paniagua, Alperen Dedeoglu +3
In deep learning models, learning more with less data is becoming more important. This paper explores how neural networks with normalized Radial Basis Function (RBF) kernels can be…
Chameleon: Learning Model Initializations Across Tasks With Different Schemas
Lukas Brinkmeyer, Rafael Rego Drumond, Randolf Scholz +2
Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization. Recent work shows that a specific initial pa…
Learning Surrogate Losses
Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme
The minimization of loss functions is the heart and soul of Machine Learning. In this paper, we propose an off-the-shelf optimization approach that can minimize virtually any non-d…