15 citations · 19 across the 8 of their papers we have counts for
8 papers
Infant Spontaneous Movement Noise Improves Exploration in Deep RL
Francisco M. López, Markus R. Ernst, Francisco Cruz +2
Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise. However, recent works show that temporally correlated colored noise…
ARROW: Augmented Replay for RObust World models
Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst +3
Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future task…
Self-Supervised Learning of Color Constancy
Markus R. Ernst, Francisco M. López, Arthur Aubret +2
Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions. While CC and its…
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Dominik Mattern, Pierre Schumacher, Francisco M. López +4
Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the…
Time to augment self-supervised visual representation learning
Arthur Aubret, Markus Ernst, Céline Teulière +1
Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to majo…
Recurrent Feedback Improves Recognition of Partially Occluded Objects
Markus Roland Ernst, Jochen Triesch, Thomas Burwick
Recurrent connectivity in the visual cortex is believed to aid object recognition for challenging conditions such as occlusion. Here we investigate if and how artificial neural net…