7 citations · 20 across the 8 of their papers we have counts for
8 papers
Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI
Fabian Mager, Lars Kai Hansen
Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high. We present COJEPA, a self-supervised framew…
On the modality gap and the contrastive loss in multi-modal representation learning
Fabian Mager, Hiba Nassar, Lars Kai Hansen
We study the modality gap in CLIP-style dual-encoder contrastive learning, where image and text embeddings remain misaligned despite being trained in a shared space. We argue that…
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt +5
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a…
Robustness of Visual Explanations to Common Data Augmentation
Lenka Tětková, Lars Kai Hansen
As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but…
On the role of Model Uncertainties in Bayesian Optimization
Jonathan Foldager, Mikkel Jordahn, Lars Kai Hansen +1
Bayesian optimization (BO) is a popular method for black-box optimization, which relies on uncertainty as part of its decision-making process when deciding which experiment to perf…
Noise-Assisted Variational Quantum Thermalization
Jonathan Foldager, Arthur Pesah, Lars Kai Hansen
Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circui…
Minimal Model Structure Analysis for Input Reconstruction in Federated Learning
Jia Qian, Hiba Nassar, Lars Kai Hansen
\ac{fl} proposed a distributed \ac{ml} framework where every distributed worker owns a complete copy of global model and their own data. The training is occurred locally, which ass…
On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline
Christoffer Riis, Damian Konrad Kowalczyk, Lars Kai Hansen
Our global population contributes visual content on platforms like Instagram, attempting to express themselves and engage their audiences, at an unprecedented and increasing rate.…