Tracking Indistinguishable Translucent Objects over Time using Weakly Supervised Structured Learning

no code implementations • CVPR 2014 • Luca Fiaschi, Ferran Diego, Konstantin Gregor, Martin Schiegg, Ullrich Koethe, Marta Zlatic, Fred A. Hamprecht

We use weakly supervised structured learning to track and disambiguate the identity of multiple indistinguishable, translucent and deformable objects that can overlap for many frames.

Semiparametric spectral modeling of the Drosophila connectome

1 code implementation • 9 May 2017 • Carey E. Priebe, Youngser Park, Minh Tang, Avanti Athreya, Vince Lyzinski, Joshua T. Vogelstein, Yichen Qin, Ben Cocanougher, Katharina Eichler, Marta Zlatic, Albert Cardona

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome.

Position Stochastic Block Model

Distance-based Positive and Unlabeled Learning for Ranking

2 code implementations • 20 May 2020 • Hayden S. Helm, Amitabh Basu, Avanti Athreya, Youngser Park, Joshua T. Vogelstein, Carey E. Priebe, Michael Winding, Marta Zlatic, Albert Cardona, Patrick Bourke, Jonathan Larson, Marah Abdin, Piali Choudhury, Weiwei Yang, Christopher W. White

Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.

Learning-To-Rank Specificity