1 citations · 2 across the 6 of their papers we have counts for
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Catching Image Retrieval Generalization
Maksim Zhdanov, Ivan Karpukhin
The concepts of overfitting and generalization are vital for evaluating machine learning models. In this work, we show that the popular Recall@K metric depends on the number of cla…
Implicit Convolutional Kernels for Steerable CNNs
Maksim Zhdanov, Nico Hoffmann, Gabriele Cesa
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving grou…
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
Maksim Zhdanov, Lisa Randolph, Thomas Kluge +4
Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials. Reconstruction of the parameters of an…
Learning Generative Factors of EEG Data with Variational auto-encoders
Maksim Zhdanov, Saskia Steinmann, Nico Hoffmann
Electroencephalography produces high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest. We address thi…