4 citations · 5 across the 3 of their papers we have counts for
4 papers
Receptive Field Refinement for Convolutional Neural Networks Reliably Improves Predictive Performance
Mats L. Richter, Christopher Pal
Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive performance in Convolutional Neural Netw…
Exploring the Properties and Evolution of Neural Network Eigenspaces during Training
Mats L. Richter, Leila Malihi, Anne-Kathrin Patricia Windler +1
In this work we explore the information processing inside neural networks using logistic regression probes \cite{probes} and the saturation metric \cite{featurespace_saturation}. W…
Size Matters
Mats L. Richter, Wolf Byttner, Ulf Krumnack +2
Fully convolutional neural networks can process input of arbitrary size by applying a combination of downsampling and pooling. However, we find that fully convolutional image class…
Spectral Analysis of Latent Representations
Justin Shenk, Mats L. Richter, Anders Arpteg +1
We propose a metric, Layer Saturation, defined as the proportion of the number of eigenvalues needed to explain 99% of the variance of the latent representations, for analyzing the…