2 citations · 2 across the 2 of their papers we have counts for
3 papers · 1 filter
Learning Invariances with Generalised Input-Convex Neural Networks
Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber +2
Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we w…
On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures
Maxim Samarin, Volker Roth, David Belius
The Neural Tangent Kernel (NTK) is an important milestone in the ongoing effort to build a theory for deep learning. Its prediction that sufficiently wide neural networks behave as…
Deep Archetypal Analysis
Sebastian Mathias Keller, Maxim Samarin, Mario Wieser +1
"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes. The p…