1 citations · 2 across the 5 of their papers we have counts for
5 papers
SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport
Simon Roschmann, Paul Krzakala, Sonia Mazelet +2
The Platonic Representation Hypothesis posits that neural networks trained on different modalities converge toward a shared statistical model of the world. Recent work exploits thi…
Unlocking Pretrained Vision Transformers for Time Series Classification
Simon Roschmann, Quentin Bouniot, Vasilii Feofanov +2
Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundation models (TSFMs) in classification. I…
Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
Shortcut Detection with Variational Autoencoders
Nicolas M. Müller, Simon Roschmann, Shahbaz Khan +2
For real-world applications of machine learning (ML), it is essential that models make predictions based on well-generalizing features rather than spurious correlations in the data…
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks
Nicolas M. Müller, Simon Roschmann, Konstantin Böttinger
Data poisoning is one of the most relevant security threats against machine learning and data-driven technologies. Since many applications rely on untrusted training data, an attac…