Search Results for author: Maxim Samarin

Found 6 papers, 3 papers with code

Deep Archetypal Analysis

1 code implementation30 Jan 2019 Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth

"Deep Archetypal Analysis" generates latent representations of high-dimensional datasets in terms of fractions of intuitively understandable basic entities called archetypes.

Representation Learning

Learning Extremal Representations with Deep Archetypal Analysis

1 code implementation3 Feb 2020 Sebastian Mathias Keller, Maxim Samarin, Fabricio Arend Torres, Mario Wieser, Volker Roth

The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information.

Band Gap

On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures

no code implementations24 Jun 2020 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.

Open-Ended Question Answering

Learning Invariances with Generalised Input-Convex Neural Networks

no code implementations14 Apr 2022 Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber, Maxim Samarin, Volker Roth

These networks represent functions that are guaranteed to have connected level sets forming smooth manifolds on the input space.

Mesh-free Eulerian Physics-Informed Neural Networks

no code implementations3 Jun 2022 Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber, Maxim Samarin, Volker Roth

Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks.

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