Search Results for author: Rasmus Berg Palm

Found 14 papers, 11 papers with code

Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming

no code implementations14 Jun 2022 Kazuya Horibe, Kathryn Walker, Rasmus Berg Palm, Shyam Sudhakaran, Sebastian Risi

Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not.

Physical Neural Cellular Automata for 2D Shape Classification

1 code implementation14 Mar 2022 Kathryn Walker, Rasmus Berg Palm, Rodrigo Moreno Garcia, Andres Faina, Kasper Stoy, Sebastian Risi

Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries.

Classification

Variational Neural Cellular Automata

1 code implementation ICLR 2022 Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran, Sebastian Risi

Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.

Fast Game Content Adaptation Through Bayesian-based Player Modelling

no code implementations18 May 2021 Miguel González-Duque, Rasmus Berg Palm, Sebastian Risi

Current systems for DDA rely on expensive data mining, or on hand-crafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the designer to present content that is purposefully easy or difficult.

EvoCraft: A New Challenge for Open-Endedness

1 code implementation8 Dec 2020 Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, Sebastian Risi

In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion.

Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning

1 code implementation13 Nov 2020 Rasmus Berg Palm, Elias Najarro, Sebastian Risi

We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules.

Clustering Meta-Learning

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

1 code implementation15 May 2020 Miguel González-Duque, Rasmus Berg Palm, David Ha, Sebastian Risi

The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.

Bayesian Optimization

Attend, Copy, Parse -- End-to-end information extraction from documents

2 code implementations18 Dec 2018 Rasmus Berg Palm, Florian Laws, Ole Winther

We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required word-level labels.

Classification General Classification

Recurrent Relational Networks for complex relational reasoning

1 code implementation ICLR 2018 Rasmus Berg Palm, Ulrich Paquet, Ole Winther

Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models.

Relational Reasoning

Recurrent Relational Networks

6 code implementations NeurIPS 2018 Rasmus Berg Palm, Ulrich Paquet, Ole Winther

We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks.

Ranked #3 on Question Answering on bAbi (Mean Error Rate metric)

Question Answering Relational Reasoning

CloudScan - A configuration-free invoice analysis system using recurrent neural networks

1 code implementation24 Aug 2017 Rasmus Berg Palm, Ole Winther, Florian Laws

We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system.

End-to-End Information Extraction without Token-Level Supervision

1 code implementation WS 2017 Rasmus Berg Palm, Dirk Hovy, Florian Laws, Ole Winther

End-to-end (E2E) models, which take raw text as input and produce the desired output directly, need not depend on token-level labels.

Cannot find the paper you are looking for? You can Submit a new open access paper.