research
∙
04/07/2021
Evolutionary rates of information gain and decay in fluctuating environments
In this paper, we wish to investigate the dynamics of information transf...
share
research
∙
04/07/2021
Bootstrapping of memetic from genetic evolution via inter-agent selection pressures
We create an artificial system of agents (attention-based neural network...
research
∙
12/03/2019
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
In this work, we propose a novel memory-based multi-agent meta-learning ...
research
∙
08/15/2019
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model
Traditional anatomical analyses captured only a fraction of real phenomi...
research
∙
02/08/2019
Generating the support with extreme value losses
When optimizing against the mean loss over a distribution of predictions...
research
∙
12/12/2018
On the potential for open-endedness in neural networks
Natural evolution gives the impression of leading to an open-ended proce...
research
∙
06/01/2018
Being curious about the answers to questions: novelty search with learned attention
We investigate the use of attentional neural network layers in order to ...
research
∙
03/30/2018
Learning to generate classifiers
We train a network to generate mappings between training sets and classi...
research
∙
08/15/2017
Learning body-affordances to simplify action spaces
Controlling embodied agents with many actuated degrees of freedom is a c...
research
∙
02/22/2017
Counterfactual Control for Free from Generative Models
We introduce a method by which a generative model learning the joint dis...
research
∙
12/14/2016
Permutation-equivariant neural networks applied to dynamics prediction
The introduction of convolutional layers greatly advanced the performanc...
research
∙
09/01/2016
