6 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
An Overview of Prototype Formulations for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Paul Mattes +2
Prototypical part networks offer interpretable alternatives to black-box deep learning models by learning visual prototypes for classification. This work provides a comprehensive a…
Endless Jailbreaks with Bijection Learning
Brian R. Y. Huang, Maximilian Li, Leonard Tang
Despite extensive safety measures, LLMs are vulnerable to adversarial inputs, or jailbreaks, which can elicit unsafe behaviors. In this work, we introduce bijection learning, a pow…
Use the Force, Bot! -- Force-Aware ProDMP with Event-Based Replanning
Paul Werner Lödige, Maximilian Xiling Li, Rudolf Lioutikov
Movement Primitives (MPs) are a well-established method for representing and generating modular robot trajectories. This work presents FA-ProDMP, a new approach which introduces fo…
Optimal ablation for interpretability
Maximilian Li, Lucas Janson
Interpretability studies often involve tracing the flow of information through machine learning models to identify specific model components that perform relevant computations for…
Planning in a recurrent neural network that plays Sokoban
Mohammad Taufeeque, Philip Quirke, Maximilian Li +4
Planning is essential for solving complex tasks, yet the internal mechanisms underlying planning in neural networks remain poorly understood. Building on prior work, we analyze a r…