4.5k citations · 8.5k across the 90 of their papers we have counts for
14 papers · 1 filter
Variational Annealing on Graphs for Combinatorial Optimization
Sebastian Sanokowski, Wilhelm Berghammer, Sepp Hochreiter +1
Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent sol…
Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi +1
Applying a machine learning model for decision-making in the real world requires to distinguish what the model knows from what it does not. A critical factor in assessing the knowl…
Functional trustworthiness of AI systems by statistically valid testing
Bernhard Nessler, Thomas Doms, Sepp Hochreiter
The authors are concerned about the safety, health, and rights of the European citizens due to inadequate measures and procedures required by the current draft of the EU Artificial…
Linear Alignment of Vision-language Models for Image Captioning
Fabian Paischer, Markus Hofmarcher, Sepp Hochreiter +1
Recently, vision-language models like CLIP have advanced the state of the art in a variety of multi-modal tasks including image captioning and caption evaluation. Many approaches l…
Quantification of Uncertainty with Adversarial Models
Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi +2
Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic u…
Learning to Modulate pre-trained Models in RL
Thomas Schmied, Markus Hofmarcher, Fabian Paischer +2
Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific…
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Fabian Paischer, Thomas Adler, Markus Hofmarcher +1
Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate t…
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
Marius-Constantin Dinu, Markus Holzleitner, Maximilian Beck +7
We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, dra…
Context-enriched molecule representations improve few-shot drug discovery
Johannes Schimunek, Philipp Seidl, Lukas Friedrich +4
A central task in computational drug discovery is to construct models from known active molecules to find further promising molecules for subsequent screening. However, typically o…
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…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Conformal Prediction for Time Series with Modern Hopfield Networks
Andreas Auer, Martin Gauch, Daniel Klotz +1
To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are diffic…
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Philipp Seidl, Andreu Vall, Sepp Hochreiter +1
Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks. With…
G-Signatures: Global Graph Propagation With Randomized Signatures
Bernhard Schäfl, Lukas Gruber, Johannes Brandstetter +1
Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures. However, GNNs suffer from over-smoothing node information and, therefore, strugg…