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ORCIDVladimir Golkov
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2020 – today
- 2024
[c7]Moritz Zaiss, Junaid R. Rajput, Hoai Nam Dang, Vladimir Golkov, Daniel Cremers, Florian Knoll, Andreas K. Maier:
Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - GPT4MR. Bildverarbeitung für die Medizin 2024: 94-99- [i14]Fabian Bongratz, Vladimir Golkov, Lukas Mautner, Luca Della Libera, Frederik Heetmeyer, Felix Czaja, Julian Rodemann, Daniel Cremers:
How to Choose a Reinforcement-Learning Algorithm. CoRR abs/2407.20917 (2024) - 2023
- [i13]Thomas Wimmer, Vladimir Golkov, Hoai Nam Dang, Moritz Zaiss, Andreas Maier, Daniel Cremers
:
Scale-Equivariant Deep Learning for 3D Data. CoRR abs/2304.05864 (2023) - [i12]Hoai Nam Dang, Vladimir Golkov, Thomas Wimmer, Daniel Cremers, Andreas K. Maier, Moritz Zaiss:
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution. CoRR abs/2305.07524 (2023) - 2021
- [b1]Vladimir Golkov:
Deep learning and variational analysis for high-dimensional and geometric biomedical data. Technical University of Munich, Germany, 2021 - [i11]Philip Müller, Vladimir Golkov, Valentina Tomassini, Daniel Cremers:
Rotation-Equivariant Deep Learning for Diffusion MRI. CoRR abs/2102.06942 (2021) - [i10]Maximilian Mozes, Martin Schmitt, Vladimir Golkov, Hinrich Schütze, Daniel Cremers:
Scene Graph Generation for Better Image Captioning? CoRR abs/2109.11398 (2021) - 2020
- [c6]Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey L. Mendenhall, Jens Meiler, Daniel Cremers:
3D Deep Learning for Biological Function Prediction from Physical Fields. 3DV 2020: 928-937 - [i9]Vladimir Golkov, Alexander Becker, Daniel T. Plop, Daniel Cuturilo, Neda Davoudi, Jeffrey L. Mendenhall, Rocco Moretti, Jens Meiler, Daniel Cremers:
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions. CoRR abs/2007.07029 (2020) - [i8]Giorgio Fabbro, Vladimir Golkov, Thomas Kemp, Daniel Cremers:
Speech Synthesis and Control Using Differentiable DSP. CoRR abs/2010.15084 (2020)
2010 – 2019
- 2019
- [i7]Jan Schuchardt, Vladimir Golkov, Daniel Cremers:
Learning to Evolve. CoRR abs/1905.03389 (2019) - [i6]Luca Della Libera, Vladimir Golkov, Yue Zhu, Arman Mielke, Daniel Cremers:
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods. CoRR abs/1910.14594 (2019) - 2018
- [c5]Philip Häusser, Johannes Plapp, Vladimir Golkov, Elie Aljalbout, Daniel Cremers:
Associative Deep Clustering: Training a Classification Network with No Labels. GCPR 2018: 18-32 - [i5]Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Daniel Cremers:
Clustering with Deep Learning: Taxonomy and New Methods. CoRR abs/1801.07648 (2018) - [i4]Aleksei Vasilev, Vladimir Golkov, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers:
q-Space Novelty Detection with Variational Autoencoders. CoRR abs/1806.02997 (2018) - 2017
- [i3]Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey L. Mendenhall, Jens Meiler, Daniel Cremers:
3D Deep Learning for Biological Function Prediction from Physical Fields. CoRR abs/1704.04039 (2017) - [i2]Jan Kukacka, Vladimir Golkov, Daniel Cremers:
Regularization for Deep Learning: A Taxonomy. CoRR abs/1710.10686 (2017) - 2016
- [j1]Vladimir Golkov, Alexey Dosovitskiy, Jonathan I. Sperl, Marion I. Menzel, Michael Czisch, Philipp G. Sämann, Thomas Brox, Daniel Cremers:
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. IEEE Trans. Medical Imaging 35(5): 1344-1351 (2016) - [c4]Vladimir Golkov, Tim Sprenger, Jonathan Sperl, Marion I. Menzel, Michael Czisch, Philipp G. Sämann, Daniel Cremers:
Model-free novelty-based diffusion MRI. ISBI 2016: 1233-1236 - [c3]Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers:
Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images. NIPS 2016: 4215-4223 - 2015
- [c2]Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox:
FlowNet: Learning Optical Flow with Convolutional Networks. ICCV 2015: 2758-2766 - [c1]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:
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans. MICCAI (1) 2015: 37-44 - [i1]Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox:
FlowNet: Learning Optical Flow with Convolutional Networks. CoRR abs/1504.06852 (2015)
Coauthor Index
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