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ORCIDAlexander Shekhovtsov
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2020 – today
- 2024
[c27]Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov:
Symmetric Equilibrium Learning of VAEs. AISTATS 2024: 3214-3222- 2023
- [c26]Tong Wei, Yash Patel, Alexander Shekhovtsov, Jirí Matas, Daniel Barath:
Generalized Differentiable RANSAC. ICCV 2023: 17603-17614 - [c25]Alexander Shekhovtsov:
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator. ICML 2023: 30931-30955 - [i26]Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov:
Symmetric Equilibrium Learning of VAEs. CoRR abs/2307.09883 (2023) - [i25]Alexander Shekhovtsov, Georgii Zakharov:
Enumerating Complexity Revisited. CoRR abs/2312.04187 (2023) - [i24]Alexander Shekhovtsov, Georgii Zakharov:
Enumerating Complexity Revisited. Electron. Colloquium Comput. Complex. TR23 (2023) - 2022
- [c24]Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach:
VAE Approximation Error: ELBO and Exponential Families. ICLR 2022 - 2021
- [c23]Anastasiia Livochka, Alexander Shekhovtsov
:
Initialization and Transfer Learning of Stochastic Binary Networks From Real-Valued Ones. CVPR Workshops 2021: 4660-4668 - [c22]Alexander Shekhovtsov, Viktor Yanush:
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. GCPR 2021: 111-126 - [c21]Alexander Shekhovtsov:
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators. GCPR 2021: 127-141 - [i23]Dmitrij Schlesinger, Alexander Shekhovtsov, Boris Flach:
VAE Approximation Error: ELBO and Conditional Independence. CoRR abs/2102.09310 (2021) - [i22]Alexander Shekhovtsov:
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators. CoRR abs/2110.03549 (2021) - 2020
- [c20]Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020: 2775-2785 - [c19]Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock:
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. CVPR 2020: 7897-7906 - [c18]Alexander Shekhovtsov, Viktor Yanush, Boris Flach:
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. NeurIPS 2020 - [i21]Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov, Friedrich Fraundorfer, Thomas Pock:
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. CoRR abs/2003.06258 (2020) - [i20]Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. CoRR abs/2004.07715 (2020) - [i19]Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. CoRR abs/2004.08227 (2020) - [i18]Alexander Shekhovtsov, Viktor Yanush, Boris Flach:
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. CoRR abs/2006.03143 (2020) - [i17]Viktor Yanush, Alexander Shekhovtsov, Dmitry Molchanov, Dmitry P. Vetrov:
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. CoRR abs/2006.06880 (2020)
2010 – 2019
- 2019
- [c17]Alexander Shekhovtsov, Boris Flach:
Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers. ICLR (Poster) 2019 - 2018
- [j6]Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy:
Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 40(7): 1668-1682 (2018) - [c16]Alexander Shekhovtsov, Boris Flach:
Stochastic Normalizations as Bayesian Learning. ACCV (2) 2018: 463-479 - [c15]Siddharth Tourani, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. ECCV (4) 2018: 264-281 - [i16]Alexander Shekhovtsov, Boris Flach:
Normalization of Neural Networks using Analytic Variance Propagation. CoRR abs/1803.10560 (2018) - [i15]Alexander Shekhovtsov, Boris Flach, Michal Busta:
Feed-forward Uncertainty Propagation in Belief and Neural Networks. CoRR abs/1803.10590 (2018) - [i14]Alexander Shekhovtsov, Boris Flach:
Stochastic Normalizations as Bayesian Learning. CoRR abs/1811.00639 (2018) - 2017
- [c14]Patrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock:
End-to-End Training of Hybrid CNN-CRF Models for Stereo. CVPR 2017: 1456-1465 - [c13]Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter, Thomas Pock:
Scalable Full Flow with Learned Binary Descriptors. GCPR 2017: 321-332 - [i13]Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter, Thomas Pock:
Scalable Full Flow with Learned Binary Descriptors. CoRR abs/1707.06427 (2017) - [i12]Boris Flach, Alexander Shekhovtsov, Ondrej Fikar:
Generative learning for deep networks. CoRR abs/1709.08524 (2017) - 2016
- [j5]Alexander Shekhovtsov:
Higher order maximum persistency and comparison theorems. Comput. Vis. Image Underst. 143: 54-79 (2016) - [j4]Christian Payer, Michael Pienn, Zoltán Bálint, Alexander Shekhovtsov, Emina Talakic, Eszter Nagy, Andrea Olschewski, Horst Olschewski, Martin Urschler:
Automated integer programming based separation of arteries and veins from thoracic CT images. Medical Image Anal. 34: 109-122 (2016) - [j3]Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes, Christoph Schnörr, Bogdan Savchynskyy:
Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Pattern Anal. Mach. Intell. 38(7): 1370-1382 (2016) - [c12]Mengtian Li, Alexander Shekhovtsov, Daniel Huber:
Complexity of Discrete Energy Minimization Problems. ECCV (2) 2016: 834-852 - [c11]Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. NIPS 2016: 334-342 - [i11]Alexander Shekhovtsov, Christian Reinbacher, Gottfried Graber, Thomas Pock:
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization. CoRR abs/1601.06274 (2016) - [i10]Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother, Bogdan Savchynskyy:
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. CoRR abs/1606.07015 (2016) - [i9]Mengtian Li, Alexander Shekhovtsov, Daniel Huber:
Complexity of Discrete Energy Minimization Problems. CoRR abs/1607.08905 (2016) - [i8]Patrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock:
End-to-End Training of Hybrid CNN-CRF Models for Stereo. CoRR abs/1611.10229 (2016) - 2015
- [c10]Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy:
Maximum persistency via iterative relaxed inference with graphical models. CVPR 2015: 521-529 - [i7]Alexander Shekhovtsov:
Higher Order Maximum Persistency and Comparison Theorems. CoRR abs/1505.00571 (2015) - [i6]Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy:
Maximum Persistency via Iterative Relaxed Inference with Graphical Models. CoRR abs/1508.07902 (2015) - 2014
- [c9]Alexander Shekhovtsov:
Maximum Persistency in Energy Minimization. CVPR 2014: 1162-1169 - [i5]Alexander Shekhovtsov:
Maximum Persistency in Energy Minimization. CoRR abs/1404.3653 (2014) - [i4]Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes, Christoph Schnörr, Bogdan Savchynskyy:
Partial Optimality by Pruning for MAP-Inference with General Graphical Models. CoRR abs/1410.6641 (2014) - 2013
- [j2]Alexander Shekhovtsov, Václav Hlavác:
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel. Int. J. Comput. Vis. 104(3): 315-342 (2013) - 2012
- [c8]Alexander Shekhovtsov, Pushmeet Kohli, Carsten Rother:
Curvature Prior for MRF-Based Segmentation and Shape Inpainting. DAGM/OAGM Symposium 2012: 41-51 - 2011
- [c7]Alexander Shekhovtsov, Václav Hlavác:
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel. EMMCVPR 2011: 1-16 - [i3]Alexander Shekhovtsov, Václav Hlavác:
A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel. CoRR abs/1109.1146 (2011) - [i2]Alexander Shekhovtsov, Václav Hlavác:
On Partial Opimality by Auxiliary Submodular Problems. CoRR abs/1109.1149 (2011) - [i1]Alexander Shekhovtsov, Pushmeet Kohli, Carsten Rother:
Curvature Prior for MRF-based Segmentation and Shape Inpainting. CoRR abs/1109.1480 (2011) - 2010
- [c6]Alexander Shekhovtsov, Václav Hlavác:
Joint Image GMM and Shading MAP Estimation. ICPR 2010: 1360-1363
2000 – 2009
- 2009
- [c5]Michal Jancosek, Alexander Shekhovtsov, Tomás Pajdla:
Scalable multi-view stereo. ICCV Workshops 2009: 1526-1533 - 2008
- [j1]Alexander Shekhovtsov, Ivan Kovtun, Václav Hlavác:
Efficient MRF deformation model for non-rigid image matching. Comput. Vis. Image Underst. 112(1): 91-99 (2008) - [c4]Alexander Shekhovtsov, Juan D. García-Arteaga, Tomás Werner:
A discrete search method for multi-modal non-rigid image registration. CVPR Workshops 2008: 1-6 - [c3]Pushmeet Kohli, Alexander Shekhovtsov, Carsten Rother, Vladimir Kolmogorov, Philip H. S. Torr:
On partial optimality in multi-label MRFs. ICML 2008: 480-487 - 2007
- [c2]Alexander Shekhovtsov, Ivan Kovtun, Václav Hlavác:
Efficient MRF Deformation Model for Non-Rigid Image Matching. CVPR 2007 - 2004
- [c1]Dmitrij Schlesinger, Boris Flach, Alexander Shekhovtsov:
A Higher Order MRF-Model for Stereo-Reconstruction. DAGM-Symposium 2004: 440-446
Coauthor Index
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