Neuro-Symbolic

Our paper Language Models as Reasoners for Out-of-Distribution Detection has been presented at the Workshop on AI Safety Engineering (WAISE) 2024 and recieved the best paper award by popular vote. It constitutes a basic extension of our idea of Out-of-Distribution Detection with Logical Reasoning, where we replaced the prolog-based reasoning component with a LLM. Abstract Deep neural networks (DNNs) are prone to making wrong predictions with high confidence for data that does not stem from their …

Our paper Out-of-Distribution Detction with Logical Reasoning has been accepted on the WACV 2024. Abstract Machine Learning models often only generalize reliably to samples from the training distribution. Consequentially, detecting when input data is out-of-distribution (OOD) is crucial, especially in safety-critical applications. Current OOD detection methods, however, tend to be domain agnostic and often fail to incorporate valuable prior knowledge about the structure of the training …

My paper Towards Deep Anomaly Detection with Structured Knowledge Representations has been accepted on the Workshop on AI Safety Engineering at SafeComp. Abstract Machine learning models tend to only make reliable predictions for inputs that are similar to the training data. Consequentially, anomaly detection, which can be used to detect unusual inputs, is critical for ensuring the safety of machine learning agents operating in open environments. In this work, we identify and discuss several …