75 citations · 210 across the 10 of their papers we have counts for
15 papers
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
Andrea Hornakova, Timo Kaiser, Paul Swoboda +3
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). Our tracker…
Neuro-algorithmic Policies enable Fast Combinatorial Generalization
Marin Vlastelica, Michal Rolínek, Georg Martius
Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, generalization to task variations is stil…
Demystifying Inductive Biases for -VAE Based Architectures
Dominik Zietlow, Michal Rolinek, Georg Martius
The performance of -Variational-Autoencoders (-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled. On the other hand,…
Sample-efficient Cross-Entropy Method for Real-time Planning
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes +4
Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse…
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Michal Rolínek, Paul Swoboda, Dominik Zietlow +3
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contai…
Optimizing Rank-based Metrics with Blackbox Differentiation
Michal Rolínek, Vít Musil, Anselm Paulus +3
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics re…
Differentiation of Blackbox Combinatorial Solvers
Marin Vlastelica, Anselm Paulus, Vít Musil +2
Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence. One possible approach is to introduce combinatorial buil…
Variational Autoencoders Pursue PCA Directions (by Accident)
Michal Rolinek, Dominik Zietlow, Georg Martius
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) repr…
L4: Practical loss-based stepsize adaptation for deep learning
Michal Rolinek, Georg Martius
We propose a stepsize adaptation scheme for stochastic gradient descent. It operates directly with the loss function and rescales the gradient in order to make fixed predicted prog…
Efficient Optimization for Rank-based Loss Functions
Pritish Mohapatra, Michal Rolinek, C. V. Jawahar +2
The accuracy of information retrieval systems is often measured using complex loss functions such as the average precision (AP) or the normalized discounted cumulative gain (NDCG).…
Even Delta-Matroids and the Complexity of Planar Boolean CSPs
Alexandr Kazda, Vladimir Kolmogorov, Michal Rolínek
The main result of this paper is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each vari…
Effectiveness of Structural Restrictions for Hybrid CSPs
Vladimir Kolmogorov, Michal Rolinek, Rustem Takhanov
Constraint Satisfaction Problem (CSP) is a fundamental algorithmic problem that appears in many areas of Computer Science. It can be equivalently stated as computing a homomorphism…
Total variation on a tree
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 programmin…
The Complexity of General-Valued CSPs
Vladimir Kolmogorov, Andrei Krokhin, Michal Rolinek
An instance of the Valued Constraint Satisfaction Problem (VCSP) is given by a finite set of variables, a finite domain of labels, and a sum of functions, each function depending o…
Superconcentrators of Density 25.3
Vladimir Kolmogorov, Michal Rolinek
An -superconcentrator is a directed, acyclic graph with input nodes and output nodes such that every subset of the inputs and every subset of the outputs of same cardina…