75 citations · 210 across the 10 of their papers we have counts for
8 papers · 1 filter
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…