Search Results for author: Elise van der Pol

Found 16 papers, 9 papers with code

Equivariant Networks for Zero-Shot Coordination

1 code implementation21 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.

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering

no code implementations29 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).

Clustering Decision Making +3

Maximum Class Separation as Inductive Bias in One Matrix

1 code implementation17 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.

Inductive Bias Long-tail Learning +3

Multi-Agent MDP Homomorphic Networks

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.

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

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.

The Impact of Negative Sampling on Contrastive Structured World Models

1 code implementation24 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.

Contrastive Learning

Plannable Approximations to MDP Homomorphisms: Equivariance under Actions

1 code implementation27 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.

Representation Learning

Contrastive Learning of Structured World Models

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.

Atari Games Contrastive Learning +2

Visual Rationalizations in Deep Reinforcement Learning for Atari Games

no code implementations1 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.

Atari Games Decision Making +2

Hyperspherical Prototype Networks

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.

Classification General Classification +1

Beyond Local Nash Equilibria for Adversarial Networks

no code implementations18 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).

GANGs: Generative Adversarial Network Games

no code implementations2 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.

Generative Adversarial Network

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