604 citations · 1k across the 14 of their papers we have counts for
16 papers
Generation of Musical Timbres using a Text-Guided Diffusion Model
Weixuan Yuan, Qadeer Khan, Vladimir Golkov
In recent years, text-to-audio systems have achieved remarkable success, enabling the generation of complete audio segments directly from text descriptions. While these systems als…
Interactions Across Blocks in Post-Training Quantization of Large Language Models
Khasmamad Shabanovi, Lukas Wiest, Vladimir Golkov +2
Post-training quantization is widely employed to reduce the computational demands of neural networks. Typically, individual substructures, such as layers or blocks of layers, are q…
How to Choose a Reinforcement-Learning Algorithm
Fabian Bongratz, Vladimir Golkov, Lukas Mautner +5
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an…
Joint MR sequence optimization beats pure neural network approaches for spin-echo MRI super-resolution
Hoai Nam Dang, Vladimir Golkov, Thomas Wimmer +3
Current MRI super-resolution (SR) methods only use existing contrasts acquired from typical clinical sequences as input for the neural network (NN). In turbo spin echo sequences (T…
Scale-Equivariant Deep Learning for 3D Data
Thomas Wimmer, Vladimir Golkov, Hoai Nam Dang +3
The ability of convolutional neural networks (CNNs) to recognize objects regardless of their position in the image is due to the translation-equivariance of the convolutional opera…
Scene Graph Generation for Better Image Captioning?
Maximilian Mozes, Martin Schmitt, Vladimir Golkov +2
We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generate…
Rotation-Equivariant Deep Learning for Diffusion MRI
Philip Müller, Vladimir Golkov, Valentina Tomassini +1
Convolutional networks are successful, but they have recently been outperformed by new neural networks that are equivariant under rotations and translations. These new networks wor…
Speech Synthesis and Control Using Differentiable DSP
Giorgio Fabbro, Vladimir Golkov, Thomas Kemp +1
Modern text-to-speech systems are able to produce natural and high-quality speech, but speech contains factors of variation (e.g. pitch, rhythm, loudness, timbre)\ that text alone…
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions
Vladimir Golkov, Alexander Becker, Daniel T. Plop +6
Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules…
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods
Luca Della Libera, Vladimir Golkov, Yue Zhu +2
Convolutional networks are successful due to their equivariance/invariance under translations. However, rotatable data such as images, volumes, shapes, or point clouds require proc…
Learning to Evolve
Jan Schuchardt, Vladimir Golkov, Daniel Cremers
Evolution and learning are two of the fundamental mechanisms by which life adapts in order to survive and to transcend limitations. These biological phenomena inspired successful c…
q-Space Novelty Detection with Variational Autoencoders
Aleksei Vasilev, Vladimir Golkov, Marc Meissner +5
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified…
Clustering with Deep Learning: Taxonomy and New Methods
Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui +2
Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper, we propose a syst…
Regularization for Deep Learning: A Taxonomy
Jan Kukačka, Vladimir Golkov, Daniel Cremers
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from…
3D Deep Learning for Biological Function Prediction from Physical Fields
Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev +5
Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. Function is dictated…
FlowNet: Learning Optical Flow with Convolutional Networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg +6
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation ha…