808 citations · 1.4k across the 52 of their papers we have counts for
57 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Efficient Learning of Discrete-Continuous Computation Graphs
David Friede, Mathias Niepert
Numerous models for supervised and reinforcement learning benefit from combinations of discrete and continuous model components. End-to-end learnable discrete-continuous models are…
Learning Disentangled Discrete Representations
David Friede, Christian Reimers, Heiner Stuckenschmidt +1
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
Federico Errica, Mathias Niepert
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired b…