3 papers
cs.LG2025
Training Transformers with Enforced Lipschitz Constants
Laker Newhouse, R. Preston Hess, Franz Cesista +3
Neural networks are often highly sensitive to input and weight perturbations. This sensitivity has been linked to pathologies such as vulnerability to adversarial examples, diverge…
cs.LG2025
Learning to Dissipate Energy in Oscillatory State-Space Models
Jared Boyer, T. Konstantin Rusch, Daniela Rus
State-space models (SSMs) are a class of networks for sequence learning that benefit from fixed state size and linear complexity with respect to sequence length, contrasting the qu…
cs.CV2025
Exploring Modality Guidance to Enhance VFM-based Feature Fusion for UDA in 3D Semantic Segmentation
Johannes Spoecklberger, Wei Lin, Pedro Hermosilla +3
Vision Foundation Models (VFMs) have become a de facto choice for many downstream vision tasks, like image classification, image segmentation, and object localization. However, the…