7 papers
Grounding Functional Similarity by Invariance-Aware Model Stitching
Ioannis Athanasiadis, Anmar Karmush, Michael Felsberg
In deep learning, functional similarity evaluation quantifies the extent to which independently trained models learn similar input--output relationships. In model stitching, functi…
Benchmark Dataset for Catalysis on 2D MXenes
Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4
Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) T…
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
Arvi Jonnarth, Ola Johansson, Jie Zhao +1
Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and…
Prior Learning in Introspective VAEs
Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg
Variational Autoencoders (VAEs) are a popular framework for unsupervised learning and data generation. A plethora of methods have been proposed focusing on improving VAEs, with the…
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving
Alkis Sygkounas, Ioannis Athanasiadis, Andreas Persson +2
Integrating human expertise with machine learning is crucial for applications demanding high accuracy and safety, such as autonomous driving. This study introduces Interactive Doub…
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling +6
Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance…