62 citations · 129 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
Embodied vision for learning object representations
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto clo…
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data
Diyuan Lu, Gerhard Kurz, Nenad Polomac +3
We apply deep learning (DL) on Magnetic resonance spectroscopy (MRS) data for the task of brain tumor detection. Medical applications often suffer from data scarcity and corruption…
Learning Abstract Representations through Lossy Compression of Multi-Modal Signals
Charles Wilmot, Gianluca Baldassarre, Jochen Triesch
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific det…
Towards Early Diagnosis of Epilepsy from EEG Data
Diyuan Lu, Sebastian Bauer, Valentin Neubert +3
Epilepsy is one of the most common neurological disorders, affecting about 1% of the population at all ages. Detecting the development of epilepsy, i.e., epileptogenesis (EPG), bef…
Residual Deep Convolutional Neural Network for EEG Signal Classification in Epilepsy
Diyuan Lu, Jochen Triesch
Epilepsy is the fourth most common neurological disorder, affecting about 1% of the population at all ages. As many as 60% of people with epilepsy experience focal seizures which o…