51 citations · 260 across the 30 of their papers we have counts for
7 papers · 1 filter
Self-Adapting Goals Allow Transfer of Predictive Models to New Tasks
Kai Olav Ellefsen, Jim Torresen
A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the sa…
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks
Charles P Martin, Jim Torresen
This paper is about creating digital musical instruments where a predictive neural network model is integrated into the interactive system. Rather than predicting symbolic music (e…
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image using CNN
Justinas Miseikis, Inka Brijacak, Saeed Yahyanejad +3
Collaborative robots are becoming more common on factory floors as well as regular environments, however, their safety still is not a fully solved issue. Collision detection does n…
Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds
Tønnes Frostad Nygaard, Charles Patrick Martin, Jim Torresen +1
The complexity of a legged robot's environment or task can inform how specialised its gait must be to ensure success. Evolving specialised robotic gaits demands many evaluations -…
Data Driven Analysis of Tiny Touchscreen Performance with MicroJam
Charles P Martin, Jim Torresen
The widespread adoption of mobile devices, such as smartphones and tablets, has made touchscreens a common interface for musical performance. New mobile musical instruments have be…
Guiding Neuroevolution with Structural Objectives
Kai Olav Ellefsen, Joost Huizinga, Jim Torresen
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be…
How do Mixture Density RNNs Predict the Future?
Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, w…