no code implementations • ICLR 2019 • Pascal Mettes, Elise van der Pol, Cees G. M. Snoek
The structure is defined by polar prototypes, points on the hypersphere of the output space.
no code implementations • 21 Jun 2023 • Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi, Abhinav Kumar, Elise van der Pol, Robin Walters, Thomas Kipf, Jan-Willem van de Meent, Lawson L. S. Wong, Robert Platt
We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration.
1 code implementation • 21 Oct 2022 • Darius Muglich, Christian Schroeder de Witt, Elise van der Pol, Shimon Whiteson, Jakob Foerster
Successful coordination in Dec-POMDPs requires agents to adopt robust strategies and interpretable styles of play for their partner.
no code implementations • 29 Jul 2022 • Elise van der Pol, Ian Gemp, Yoram Bachrach, Richard Everett
A core step of spectral clustering is performing an eigendecomposition of the corresponding graph Laplacian matrix (or equivalently, a singular value decomposition, SVD, of the incidence matrix).
1 code implementation • 17 Jun 2022 • Tejaswi Kasarla, Gertjan J. Burghouts, Max van Spengler, Elise van der Pol, Rita Cucchiara, Pascal Mettes
This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations.
1 code implementation • ICLR 2022 • Elise van der Pol, Herke van Hoof, Frans A. Oliehoek, Max Welling
This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information, yet is able to share experience between global symmetries in the joint state-action space of cooperative multi-agent systems.
2 code implementations • ICLR 2022 • Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling
Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry.
1 code implementation • 24 Jul 2021 • Ondrej Biza, Elise van der Pol, Thomas Kipf
World models trained by contrastive learning are a compelling alternative to autoencoder-based world models, which learn by reconstructing pixel states.
2 code implementations • NeurIPS 2020 • Elise van der Pol, Daniel E. Worrall, Herke van Hoof, Frans A. Oliehoek, Max Welling
MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint state-action space of an MDP.
1 code implementation • 27 Feb 2020 • Elise van der Pol, Thomas Kipf, Frans A. Oliehoek, Max Welling
We introduce a contrastive loss function that enforces action equivariance on the learned representations.
3 code implementations • ICLR 2020 • Thomas Kipf, Elise van der Pol, Max Welling
Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
no code implementations • 1 Feb 2019 • Laurens Weitkamp, Elise van der Pol, Zeynep Akata
Due to the capability of deep learning to perform well in high dimensional problems, deep reinforcement learning agents perform well in challenging tasks such as Atari 2600 games.
1 code implementation • NeurIPS 2019 • Pascal Mettes, Elise van der Pol, Cees G. M. Snoek
This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces.
no code implementations • 18 Jun 2018 • Frans A. Oliehoek, Rahul Savani, Jose Gallego, Elise van der Pol, Roderich Groß
Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE).
no code implementations • 2 Dec 2017 • Frans A. Oliehoek, Rahul Savani, Jose Gallego-Posada, Elise van der Pol, Edwin D. de Jong, Roderich Gross
We introduce Generative Adversarial Network Games (GANGs), which explicitly model a finite zero-sum game between a generator ($G$) and classifier ($C$) that use mixed strategies.