Vladimir Golkov

is this you? claim profile

∙

0 followers

∙

TU München

∙

Technische Universität München

Doctoral Candidate at Technical University of Munich, Doctoral Candidate at GE Global Research from 2012-2014, Intern at GE Global Research from 2011-2012, Intern at Munich Airport from 2009-2010 Sofware Development

research

∙ 09/23/2021

Scene Graph Generation for Better Image Captioning?

We investigate the incorporation of visual relationships into the task o...

Maximilian Mozes, et al. ∙

research

∙ 02/13/2021

Rotation-Equivariant Deep Learning for Diffusion MRI

Convolutional networks are successful, but they have recently been outpe...

Philip Müller, et al. ∙

research

∙ 10/28/2020

Speech Synthesis and Control Using Differentiable DSP

Modern text-to-speech systems are able to produce natural and high-quali...

Giorgio Fabbro, et al. ∙

research

∙ 10/31/2019

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

One of the reasons for the success of convolutional networks is their eq...

Luca Della Libera, et al. ∙

research

∙ 05/08/2019

Learning to Evolve

Evolution and learning are two of the fundamental mechanisms by which li...

Jan Schuchardt, et al. ∙

research

∙ 06/08/2018

q-Space Novelty Detection with Variational Autoencoders

In machine learning, novelty detection is the task of identifying novel ...

Aleksei Vasilev, et al. ∙

research

∙ 01/23/2018

Clustering with Deep Learning: Taxonomy and New Methods

Clustering is a fundamental machine learning method. The quality of its ...

Elie Aljalbout, et al. ∙

research

∙ 10/29/2017

Regularization for Deep Learning: A Taxonomy

Regularization is one of the crucial ingredients of deep learning, yet t...

Jan Kukačka, et al. ∙

research

∙ 04/13/2017

3D Deep Learning for Biological Function Prediction from Physical Fields

Predicting the biological function of molecules, be it proteins or drug-...

Vladimir Golkov, et al. ∙

research

∙ 04/26/2015

FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful ...

Philipp Fischer, et al. ∙

Vladimir Golkov

Featured Co-authors

Daniel Cremers

Hinrich Schütze

Thomas Brox

Alexey Dosovitskiy

Patrick van der Smagt

Eddy Ilg

Martin Schmitt

Caner Hazirbas

Elie Aljalbout

Maximilian Mozes

Atanas Mirchev

Scene Graph Generation for Better Image Captioning?

Rotation-Equivariant Deep Learning for Diffusion MRI

Speech Synthesis and Control Using Differentiable DSP

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

Learning to Evolve

q-Space Novelty Detection with Variational Autoencoders

Clustering with Deep Learning: Taxonomy and New Methods

Regularization for Deep Learning: A Taxonomy

3D Deep Learning for Biological Function Prediction from Physical Fields

FlowNet: Learning Optical Flow with Convolutional Networks