← back
L

Laurin Lux

ORCID ↗ h-index 3
2.6
Founder Score · low confidence
1/5 signal groups
Track record 0.0/30 Career 0.0/20 Shipping 0.0/30 Research 2.6/10 Network 0.0/10
Research
  • h-index 3
  • 2 recent paper(s)

Opportunities

all opportunities ▸

Internal company pages for screening and memo — not external source links.

Beyond scalar losses: calibrating segmentation models via gradient vector field
Region-based loss functions, such as the Dice loss, have established themselves as the de facto standard for highly class- and region-imbalanced segmentation tasks. However, models
founded · arXiv · research
Open ▸

Activation — cold outreach, not cold investment

Generates a personal note referencing this founder's actual work, inviting a real application.

▸ Add / edit details

On save, the model re-reads scraped profile text plus your investor details, then rebuilds Experience / Education (adds and removes) and refreshes the score.

Ventures

Beyond scalar losses: calibrating segmentation models via gradient vector field
founded
Opportunity ▸

Experience

Cornell University
affiliated with · institution
Enriched
Munich Center for Machine Learning
affiliated with · institution
Enriched
Technical University of Munich
affiliated with · institution
Enriched

Output & traction

Beyond scalar losses: calibrating segmentation models via gradient vector field surgery
authored · 2026 · paper
arXiv