15 citations · 28 across the 8 of their papers we have counts for
13 papers
Is an encoder within reach?
Helene Hauschultz, Rasmus Berg Palm. Pablo Moreno-Muños, Nicki Skafte Detlefsen +2
The encoder network of an autoencoder is an approximation of the nearest point projection onto the manifold spanned by the decoder. A concern with this approximation is that, while…
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry
Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg +1
Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to en…
Physical Neural Cellular Automata for 2D Shape Classification
Kathryn Walker, Rasmus Berg Palm, Rodrigo Moreno Garcia +3
Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. Biological systems possess the ab…
Variational Neural Cellular Automata
Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran +1
In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all create…
Fast Game Content Adaptation Through Bayesian-based Player Modelling
Miguel González-Duque, Rasmus Berg Palm, Sebastian Risi
In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this g…
EvoCraft: A New Challenge for Open-Endedness
Djordje Grbic, Rasmus Berg Palm, Elias Najarro +2
This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicati…
Evolutionary Planning in Latent Space
Thor V. A. N. Olesen, Dennis T. T. Nguyen, Rasmus Berg Palm +1
Planning is a powerful approach to reinforcement learning with several desirable properties. However, it requires a model of the world, which is not readily available in many real-…
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning
Rasmus Berg Palm, Elias Najarro, Sebastian Risi
Hebbian meta-learning has recently shown promise to solve hard reinforcement learning problems, allowing agents to adapt to some degree to changes in the environment. However, beca…
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
Miguel González-Duque, Rasmus Berg Palm, David Ha +1
Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of…
Attend, Copy, Parse -- End-to-end information extraction from documents
Rasmus Berg Palm, Florian Laws, Ole Winther
Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This end-to-end data is naturally c…
Recurrent Relational Networks
Rasmus Berg Palm, Ulrich Paquet, Ole Winther
This paper is concerned with learning to solve tasks that require a chain of interdependent steps of relational inference, like answering complex questions about the relationships…
CloudScan - A configuration-free invoice analysis system using recurrent neural networks
Rasmus Berg Palm, Ole Winther, Florian Laws
We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoi…
End-to-End Information Extraction without Token-Level Supervision
Rasmus Berg Palm, Dirk Hovy, Florian Laws +1
Most state-of-the-art information extraction approaches rely on token-level labels to find the areas of interest in text. Unfortunately, these labels are time-consuming and costly…