25 citations · 31 across the 4 of their papers we have counts for
6 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…
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…