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07/19/2023
Symmetric Equilibrium Learning of VAEs
We view variational autoencoders (VAE) as decoder-encoder pairs, which m...
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10/07/2021
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators
Discrete and especially binary random variables occur in many machine le...
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02/18/2021
VAE Approximation Error: ELBO and Conditional Independence
The importance of Variational Autoencoders reaches far beyond standalone...
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06/11/2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Training neural networks with binary weights and activations is a challe...
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06/04/2020
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
In networks with binary activations and or binary weights the training b...
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04/16/2020
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
Dense, discrete Graphical Models with pairwise potentials are a powerful...
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04/16/2020
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization
We consider the maximum-a-posteriori inference problem in discrete graph...
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03/13/2020
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
It has been proposed by many researchers that combining deep neural netw...
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11/01/2018
Stochastic Normalizations as Bayesian Learning
In this work we investigate the reasons why Batch Normalization (BN) imp...
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03/28/2018
Feed-forward Uncertainty Propagation in Belief and Neural Networks
We propose a feed-forward inference method applicable to belief and neur...
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03/28/2018
Normalization of Neural Networks using Analytic Variance Propagation
We address the problem of estimating statistics of hidden units in a neu...
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09/25/2017
Generative learning for deep networks
Learning, taking into account full distribution of the data, referred to...
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07/20/2017
Scalable Full Flow with Learned Binary Descriptors
We propose a method for large displacement optical flow in which local m...
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11/30/2016
End-to-End Training of Hybrid CNN-CRF Models for Stereo
We propose a novel and principled hybrid CNN+CRF model for stereo estima...
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07/29/2016
Complexity of Discrete Energy Minimization Problems
Discrete energy minimization is widely-used in computer vision and machi...
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06/22/2016
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
We consider the problem of jointly inferring the M-best diverse labeling...
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01/23/2016
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
Dense image matching is a fundamental low-level problem in Computer Visi...
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08/31/2015
Maximum Persistency via Iterative Relaxed Inference with Graphical Models
We consider the NP-hard problem of MAP-inference for undirected discrete...
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05/04/2015
Higher Order Maximum Persistency and Comparison Theorems
We address combinatorial problems that can be formulated as minimization...
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10/24/2014
Partial Optimality by Pruning for MAP-Inference with General Graphical Models
We consider the energy minimization problem for undirected graphical mod...
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09/07/2011
Curvature Prior for MRF-based Segmentation and Shape Inpainting
Most image labeling problems such as segmentation and image reconstructi...
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09/06/2011
