2 code implementations • ICCV 2021 • Andrea Hornakova, Timo Kaiser, Paul Swoboda, Michal Rolinek, Bodo Rosenhahn, Roberto Henschel
We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).
no code implementations • 12 Feb 2021 • Dominik Zietlow, Michal Rolinek, Georg Martius
By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures.
no code implementations • 1 Jan 2021 • Marin Vlastelica Pogančić, Michal Rolinek, Georg Martius
Although model-based and model-free approaches to learning control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
no code implementations • 1 Jan 2021 • Dominik Zietlow, Michal Rolinek, Georg Martius
The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled.
no code implementations • NeurIPS Workshop LMCA 2020 • Marin Vlastelica Pogančić, Michal Rolinek, Georg Martius
Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
no code implementations • NeurIPS Workshop LMCA 2020 • Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, Georg Martius
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.
1 code implementation • 14 Aug 2020 • Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, Georg Martius
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning
reinforcement-learning
+1
no code implementations • CVPR 2020 • Michal Rolinek, Vit Musil, Anselm Paulus, Marin Vlastelica, Claudio Michaelis, Georg Martius
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
no code implementations • CVPR 2019 • Michal Rolinek, Dominik Zietlow, Georg Martius
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.
2 code implementations • NeurIPS 2018 • Michal Rolinek, Georg Martius
We propose a stepsize adaptation scheme for stochastic gradient descent.
no code implementations • CVPR 2018 • Pritish Mohapatra, Michal Rolinek, C. V. Jawahar, Vladimir Kolmogorov, M. Pawan Kumar
We provide a complete characterization of the loss functions that are amenable to our algorithm, and show that it includes both AP and NDCG based loss functions.
no code implementations • 26 Feb 2015 • Vladimir Kolmogorov, Thomas Pock, Michal Rolinek
We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems.