Neural-Symbolic Learning and Reasoning

.- Context Helps: Integrating context information with videos in a graph-based HAR framework.

.- Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Models.

.- Variable Assignment Invariant Neural Networks for Learning Logic Programs.

.- ViPro: Enabling and Controlling Video Prediction for Complex Dynamical Scenarios using Procedural Knowledge.

.- The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning.

.- A semantic loss for ontology classification.

.- On the use of Neurosymbolic AI for Defending against Cyber Attacks.

.- Bayesian Inverse Graphics for Few-Shot Concept Learning.

.- Simple and Effective Transfer Learning for Neuro-Symbolic Integration.

.- Ethical Reward Machines.

.- Embed2Rule - Scalable Neuro-Symbolic Learning via Latent Space Weak-Labelling.

.- ULLER: A Unified Language for Learning and Reasoning.

.- Disentangling Visual Priors: Unsupervised Learning of Scene Interpretations with Compositional Autoencoder.

.- Probing LLMs for logical reasoning.

.- Enhancing Machine Learning Predictions through Knowledge Graph Embeddings.

.- Terminating Differentiable Tree Experts.

.- Valid Text-to-SQL Generation with Unification-based DeepStochLog.

.- Enhancing Geometric Ontology Embeddings for EL++ with Negative Sampling and Deductive Closure Filtering.

.- Lattice-preserving ALC ontology embeddings.

.- Towards Learning Abductive Reasoning using VSA Distributed Representations.

.- Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation.

.- Leveraging Neurosymbolic AI for Slice Discovery.