Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps - tfrerix/proxprop
by standard backpropagation algorithmn (BPNN) [7]. Although BPNN proved to be efficient in some applications, its convergence rate is relatively slow and it often yields sub-optimal solutions ([10], =-=[11]-=-, [12]). BPNN learning basically is a hill climbing technique. Therefore, ...
The Beast algorithm (Zhao et al., 2013) is also available in python and Matlab and I’m sure there are many others. Matlab offers several native solutions, including the findchangepts function. For comparison, here I used a basic change point detection method that relies on differences in m...
As in ref.29, in each cell, we model gene activation and subsequent active transcription as stochastic multistep processes. Here, in addition, we impose that the rate of each step is dependent on the molecule number of specific molecular species (Fig.1A and B). Specifically, the inducers’ ...
several processes, we can see that PHM gets involved in many areas, such as sensor systems, failure mechanisms and algorithm designs. The implementation of PHM is a complicated course. Difficulties of making PHM come true are from many aspects. Firstly, it is related to integrated subjects so ...
We propose a silent self-stabilizing leader election algorithm for bidirectional arbitrary connected identified networks. This algorithm is written in the locally shared memory model under the distributed unfair daemon. It requires no global knowledge on the network. Its stabilization time is in Θ(n3...
Although a matrix inversion is in general an expensive O(N3) operation, the matrix in Equation (13) is tridiagonal, which can be inverted e ciently using the Thomas 10 algorithm which is O(N). Furthermore, the scheme is unconditionally stable, and in the discrete scale-space framework one...
In this section, we first introduce the mathematical framework as presented in the original STAPLE algorithm by Warfield et al. (2004). We then introduce the idea of global and local ranking and the subsequent STAPLE model changes. Finally, we extend the full framework to a multi-label scenari...
Intel VTune Profiler helps design an efficient algorithm by: Locating the most time and memory-consuming part of your code. Locating microarchitecture bottlenecks and identifying the most significant hardware issues and memory-access related issues. ...
This tracking model will let the algorithm know the best way to overlap and display the images so a positional agreement can be reached when the map can be generated. Finally, the point cloud model is reconstructed based on the tracking model. Following the tracking model, images are gradually...