4.5k citations · 6.6k across the 6 of their papers we have counts for
9 papers · 1 filter
Set Learning for Accurate and Calibrated Models
Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang +2
Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a…
Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner +5
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learn…
Interpretable Deep Learning in Drug Discovery
Kristina Preuer, Günter Klambauer, Friedrich Rippmann +2
Without any means of interpretation, neural networks that predict molecular properties and bioactivities are merely black boxes. We will unravel these black boxes and will demonstr…
RUDDER: Return Decomposition for Delayed Rewards
Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich +3
We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate…
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner +2
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such…
First Order Generative Adversarial Networks
Calvin Seward, Thomas Unterthiner, Urs Bergmann +2
GANs excel at learning high dimensional distributions, but they can update generator parameters in directions that do not correspond to the steepest descent direction of the object…