398 citations
- Data61AU6 papers
- Ghent UniversityBE3 papers
- University of Applied Sciences and Arts of Southern SwitzerlandCH3 papers
- Hokkaido UniversityJP2 papers
- Technical University of MunichDE2 papers
- ArcelorMittal (France)FR1 paper
- École Nationale Supérieure des Mines de ParisFR1 paper
- Indian Institute of Technology GuwahatiIN1 paper
- Laboratoire d’Informatique Fondamentale de MarseilleFR1 paper
- Max Planck SocietyDE1 paper
- McGill UniversityCA1 paper
- Tata Institute of Fundamental ResearchIN1 paper
9 papers · 1 filter
A Learning Framework for Morphological Operators using Counter-Harmonic Mean
Jonathan Masci, Jesús Angulo, Jürgen Schmidhuber
We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough ex…
The Complexity of Approximately Solving Influence Diagrams
Denis D. Maua, Cassio Polpo de Campos, Marco Zaffalon
Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described…
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices
Yi Sun, Faustino Gomez, Juergen Schmidhuber
We present a new way of converting a reversible finite Markov chain into a non-reversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based…
LP Rounding for k-Centers with Non-uniform Hard Capacities
Marek Cygan, MohammadTaghi Hajiaghayi, Samir Khuller
In this paper we consider a generalization of the classical k-center problem with capacities. Our goal is to select k centers in a graph, and assign each node to a nearby center, s…
Object Recognition with Multi-Scale Pyramidal Pooling Networks
Jonathan Masci, Ueli Meier, Gabriel Fricout +1
We present a Multi-Scale Pyramidal Pooling Network, featuring a novel pyramidal pooling layer at multiple scales and a novel encoding layer. Thanks to the former the network does n…
Multimodal similarity-preserving hashing
Jonathan Masci, Michael M. Bronstein, Alexander A. Bronstein +1
We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The pr…