We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and...
Ashish Venugopal, Jakob Uszkoreit, David Talbot, F...
We present a two-level Boolean minimization tool (BOOM) based on a new implicant generation paradigm. In contrast to all previous minimization methods, where the implicants are ge...
This work presents a new approach to contour representation and coding. It consists of an improved fitting of high-degree (4th to 18th ) implicit polynomials (IPs) to the contour,...
This paper is concerned with the problem of predicting relative performance of classification algorithms. It focusses on methods that use results on small samples and discusses th...
This paper proposes a new interactive hybrid non-rigid registration framework that combines any intensity-based algorithm with a feature-based component, using an iterative dual e...
Antoine Azar, Chenyang Xu, Xavier Pennec, Nicholas...