29 citations · 29 across the 4 of their papers we have counts for
8 papers
Neural Network Approximation of Refinable Functions
Ingrid Daubechies, Ronald DeVore, Nadav Dym +6
In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approxi…
Non-Convex Planar Harmonic Maps
Shahar Z. Kovalsky, Noam Aigerman, Ingrid Daubechies +3
We formulate a novel characterization of a family of invertible maps between two-dimensional domains. Our work follows two classic results: The Radó-Kneser-Choquet (RKC) theorem, w…
Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration
Nadav Dym, Shahar Ziv Kovalsky
In recent years, several branch-and-bound (BnB) algorithms have been proposed to globally optimize rigid registration problems. In this paper, we suggest a general framework to imp…
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images
David Dov, Shahar Ziv Kovalsky, Serge Assaad +4
We consider machine-learning-based thyroid-malignancy prediction from cytopathology whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for the an…
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
David Dov, Shahar Kovalsky, Jonathan Cohen +3
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach…
ariaDNE: A Robustly Implemented Algorithm for Dirichlet Energy of the Normal
Shan Shan, Shahar Z. Kovalsky, Julie M. Winchester +2
Point 1: Shape characterizers are metrics that quantify aspects of the overall geometry of a 3D digital surface. When computed for biological objects, the values of a shape charact…