6 citations · 12 across the 10 of their papers we have counts for
11 papers
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…
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…
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…
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…
Quantum Classification through Tournament Voting for Robust Single-Shot Inference
Anastasja D. Helgesen, Jan-Åke Larsson, Michael Felsberg
Quantum machine learning (QML) promises powerful classification capabilities, but suffers from fragile output encodings and high sampling demands -- especially in multiclass settin…
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…
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
William Ljungbergh, Adam Tonderski, Joakim Johnander +4
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation…
SeTformer is What You Need for Vision and Language
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
The dot product self-attention (DPSA) is a fundamental component of transformers. However, scaling them to long sequences, like documents or high-resolution images, becomes prohibi…
Certainty In, Certainty Out: REVQCs for Quantum Machine Learning
Hannah Helgesen, Michael Felsberg, Jan-Åke Larsson
The field of Quantum Machine Learning (QML) has emerged recently in the hopes of finding new machine learning protocols or exponential speedups for classical ones. Apart from probl…
IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation
Qiyu Sun, Huilin Chen, Meng Zheng +3
Domain generalized semantic segmentation (DGSS) is a critical yet challenging task, where the model is trained only on source data without access to any target data. Despite the pr…