A Higher Order MRF-Model for Stereo-Reconstruction. - Dmitrij Schlesinger; Boris Flach; Alexander Shekhovtsov (DAGM-Symposium, 2004)

A discrete search method for multi-modal non-rigid image registration. - Alexander Shekhovtsov; Juan D. García-Arteaga; Tomás Werner (CVPR Workshops, 2008)

On partial optimality in multi-label MRFs. - Pushmeet Kohli; Alexander Shekhovtsov; Carsten Rother; Vladimir Kolmogorov; Philip H. S. Torr (ICML, 2008)

Efficient MRF deformation model for non-rigid image matching. - Alexander Shekhovtsov; Ivan Kovtun; Václav Hlavác (Comput. Vis. Image Underst., 2008)

Scalable multi-view stereo. - Michal Jancosek; Alexander Shekhovtsov; Tomás Pajdla (ICCV Workshops, 2009)

Joint Image GMM and Shading MAP Estimation. - Alexander Shekhovtsov; Václav Hlavác (ICPR, 2010)

On Partial Opimality by Auxiliary Submodular Problems - Alexander Shekhovtsov; Václav Hlavác (CoRR, 2011)

Curvature Prior for MRF-Based Segmentation and Shape Inpainting. - Alexander Shekhovtsov; Pushmeet Kohli; Carsten Rother (DAGM/OAGM Symposium, 2012)

A Distributed Mincut/Maxflow Algorithm Combining Path Augmentation and Push-Relabel. - Alexander Shekhovtsov; Václav Hlavác (Int. J. Comput. Vis., 2013)

Maximum Persistency in Energy Minimization. - Alexander Shekhovtsov (CVPR, 2014)

Complexity of Discrete Energy Minimization Problems. - Mengtian Li; Alexander Shekhovtsov; Daniel Huber (ECCV (2), 2016)

Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization. - Alexander Kirillov; Alexander Shekhovtsov; Carsten Rother; Bogdan Savchynskyy (NIPS, 2016)

Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization. - Alexander Shekhovtsov; Christian Reinbacher; Gottfried Graber; Thomas Pock (CoRR, 2016)

Higher order maximum persistency and comparison theorems. - Alexander Shekhovtsov (Comput. Vis. Image Underst., 2016)

Automated integer programming based separation of arteries and veins from thoracic CT images. - Christian Payer; Michael Pienn; Zoltán Bálint; Alexander Shekhovtsov; Emina Talakic; Eszter Nagy; Andrea Olschewski; Horst Olschewski; Martin Urschler (Medical Image Anal., 2016)

Partial Optimality by Pruning for MAP-Inference with General Graphical Models. - Paul Swoboda; Alexander Shekhovtsov; Jörg Hendrik Kappes; Christoph Schnörr; Bogdan Savchynskyy (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

End-to-End Training of Hybrid CNN-CRF Models for Stereo. - Patrick Knöbelreiter; Christian Reinbacher; Alexander Shekhovtsov; Thomas Pock (CVPR, 2017)

Scalable Full Flow with Learned Binary Descriptors. - Gottfried Munda; Alexander Shekhovtsov; Patrick Knöbelreiter; Thomas Pock (GCPR, 2017)

Generative learning for deep networks. - Boris Flach; Alexander Shekhovtsov; Ondrej Fikar (CoRR, 2017)

Stochastic Normalizations as Bayesian Learning. - Alexander Shekhovtsov; Boris Flach (ACCV (2), 2018)

MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. - Siddharth Tourani; Alexander Shekhovtsov; Carsten Rother; Bogdan Savchynskyy (ECCV (4), 2018)

Normalization of Neural Networks using Analytic Variance Propagation. - Alexander Shekhovtsov; Boris Flach (CoRR, 2018)

Feed-forward Uncertainty Propagation in Belief and Neural Networks. - Alexander Shekhovtsov; Boris Flach; Michal Busta (CoRR, 2018)

Maximum Persistency via Iterative Relaxed Inference in Graphical Models. - Alexander Shekhovtsov; Paul Swoboda; Bogdan Savchynskyy (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers. - Alexander Shekhovtsov; Boris Flach (ICLR (Poster), 2019)

Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. - Siddharth Tourani; Alexander Shekhovtsov; Carsten Rother; Bogdan Savchynskyy (AISTATS, 2020)

Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. - Patrick Knöbelreiter; Christian Sormann; Alexander Shekhovtsov; Friedrich Fraundorfer; Thomas Pock (CVPR, 2020)

Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. - Alexander Shekhovtsov; Viktor Yanush; Boris Flach (NeurIPS, 2020)

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. - Viktor Yanush; Alexander Shekhovtsov; Dmitry Molchanov; Dmitry P. Vetrov (CoRR, 2020)

Initialization and Transfer Learning of Stochastic Binary Networks From Real-Valued Ones. - Anastasiia Livochka; Alexander Shekhovtsov (CVPR Workshops, 2021)

Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators. - Alexander Shekhovtsov (GCPR, 2021)

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. - Alexander Shekhovtsov; Viktor Yanush (GCPR, 2021)

VAE Approximation Error: ELBO and Conditional Independence. - Dmitrij Schlesinger; Alexander Shekhovtsov; Boris Flach (CoRR, 2021)

VAE Approximation Error: ELBO and Exponential Families. - Alexander Shekhovtsov; Dmitrij Schlesinger; Boris Flach (ICLR, 2022)

Generalized Differentiable RANSAC. - Tong Wei 002; Yash Patel; Alexander Shekhovtsov; Jirí Matas; Daniel Barath (ICCV, 2023)

Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator. - Alexander Shekhovtsov (ICML, 2023)

Enumerating Complexity Revisited. - Alexander Shekhovtsov; Georgii Zakharov (Electron. Colloquium Comput. Complex., 2023)

Symmetric Equilibrium Learning of VAEs. - Boris Flach; Dmitrij Schlesinger; Alexander Shekhovtsov (AISTATS, 2024)

Randomized Lifting to Semi-Structured Communication Complexity via Linear Diversity. - Vladimir Podolskii 001; Alexander Shekhovtsov (Electron. Colloquium Comput. Complex., 2024)