Vladimir Golkov

According to our database, Vladimir Golkov authored at least 21 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

How to Choose a Reinforcement-Learning Algorithm.

Fabian Bongratz, Vladimir Golkov, Lukas Mautner, Luca Della Libera, Frederik Heetmeyer, Felix Czaja, Julian Rodemann, Daniel Cremers

CoRR, 2024

Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - GPT4MR.

Moritz Zaiss, Junaid R. Rajput, Hoai Nam Dang, Vladimir Golkov, Daniel Cremers, Florian Knoll, Andreas K. Maier

Proceedings of the Bildverarbeitung für die Medizin 2024, 2024

2023

Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution.

Hoai Nam Dang, Vladimir Golkov, Thomas Wimmer, Daniel Cremers, Andreas K. Maier, Moritz Zaiss

CoRR, 2023

Scale-Equivariant Deep Learning for 3D Data.

Thomas Wimmer, Vladimir Golkov, Hoai Nam Dang, Moritz Zaiss, Andreas Maier, Daniel Cremers

CoRR, 2023

2021

Deep learning and variational analysis for high-dimensional and geometric biomedical data.

Vladimir Golkov

PhD thesis, 2021

Scene Graph Generation for Better Image Captioning?

Maximilian Mozes, Martin Schmitt, Vladimir Golkov, Hinrich Schütze, Daniel Cremers

CoRR, 2021

Rotation-Equivariant Deep Learning for Diffusion MRI.

Philip Müller, Vladimir Golkov, Valentina Tomassini, Daniel Cremers

CoRR, 2021

2020

Speech Synthesis and Control Using Differentiable DSP.

Giorgio Fabbro, Vladimir Golkov, Thomas Kemp, Daniel Cremers

CoRR, 2020

Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions.

Vladimir Golkov, Alexander Becker, Daniel T. Plop, Daniel Cuturilo, Neda Davoudi, Jeffrey L. Mendenhall, Rocco Moretti, Jens Meiler, Daniel Cremers

CoRR, 2020

3D Deep Learning for Biological Function Prediction from Physical Fields.

Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey L. Mendenhall, Jens Meiler, Daniel Cremers

Proceedings of the 8th International Conference on 3D Vision, 2020

2019

Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods.

Luca Della Libera, Vladimir Golkov, Yue Zhu, Arman Mielke, Daniel Cremers

CoRR, 2019

Learning to Evolve.

Jan Schuchardt, Vladimir Golkov, Daniel Cremers

CoRR, 2019

2018

q-Space Novelty Detection with Variational Autoencoders.

Aleksei Vasilev, Vladimir Golkov, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers

CoRR, 2018

Clustering with Deep Learning: Taxonomy and New Methods.

Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Daniel Cremers

CoRR, 2018

Associative Deep Clustering: Training a Classification Network with No Labels.

Philip Häusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, Daniel Cremers

Proceedings of the Pattern Recognition - 40th German Conference, 2018

2017

Regularization for Deep Learning: A Taxonomy.

Jan Kukacka, Vladimir Golkov, Daniel Cremers

CoRR, 2017

2016

q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.

Vladimir Golkov, Alexey Dosovitskiy, Jonathan I. Sperl, Marion I. Menzel, Michael Czisch, Philipp G. Sämann, Thomas Brox, Daniel Cremers

IEEE Trans. Medical Imaging, 2016

Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images.

Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Model-free novelty-based diffusion MRI.

Vladimir Golkov, Tim Sprenger, Jonathan Sperl, Marion I. Menzel, Michael Czisch, Philipp G. Sämann, Daniel Cremers

Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 2016

2015

q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans.

Vladimir Golkov, Alexey Dosovitskiy, Philipp G. Sämann, Jonathan I. Sperl, Tim Sprenger, Michael Czisch, Marion I. Menzel, Pedro A. Gómez, Axel Haase, Thomas Brox, Daniel Cremers

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015

FlowNet: Learning Optical Flow with Convolutional Networks.

Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015