30 citations · 33 across the 5 of their papers we have counts for
5 papers
Contrastive Abstraction for Reinforcement Learning
Vihang Patil, Markus Hofmarcher, Elisabeth Rumetshofer +1
Learning agents with reinforcement learning is difficult when dealing with long trajectories that involve a large number of states. To address these learning problems effectively,…
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
Johannes Lehner, Benedikt Alkin, Andreas Fürst +3
Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a…
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner +8
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation a…
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer +8
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual…
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher +5
Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving h…