1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Machine learning-assisted close-set X-ray diffraction phase identification of transition metals
Maksim Zhdanov, Andrey Zhdanov
Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to pre…
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…
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
Maksim Zhdanov, Saskia Steinmann, Nico Hoffmann
Functional connectivity plays an essential role in modern neuroscience. The modality sheds light on the brain's functional and structural aspects, including mechanisms behind multi…
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…