We present new insights and a novel paradigm (StEik) for learning implic...
We discover restrained numerical instabilities in current training pract...
Traditional cameras measure image intensity. Event cameras, by contrast,...
Deep learning (DL) 3D dose prediction has recently gained a lot of atten...
If we wish to integrate a function h|Ω⊂^n→ along a
single T-level surfac...
This paper proposes a novel training model based on shape and appearance...
Purpose: In current clinical practice, noisy and artifact-ridden weekly
...
The robustness of neural networks is challenged by adversarial examples ...
The safety and robustness of learning-based decision-making systems are ...
The ability to accurately reconstruct the 3D facets of a scene is one of...
We further develop a new framework, called PDE Acceleration, by applying...
We consider the problem of optimization of cost functionals on the
infin...
Two major uncertainties, dataset bias and perturbation, prevail in
state...
Following the seminal work of Nesterov, accelerated optimization methods...
In this paper, we propose a method for tracking structures (e.g., ventri...
We present a compact formula for the derivative of a 3-D rotation matrix...