To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured ...
The intuition behind the backpropagation algorithm is as follows. Given a training example (x,y), we will first run a "forward pass" to compute all the activations throughout the network, including the output value of the hypothesishW,b(x). Then, for each nodeiin layerl, we would like...
Effect of Free Parameters: There are two free param- eters in the algorithm design, i.e., µ for the decoder weight loss and λ for the sparsity control, as shown in Eq. (10). We keep µ = 1e−6 during the experiments. Fig. 11 shows how RMSE is decreasing when we increase...
Anautoencoderneural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. I.e., it uses . Here is an autoencoder: we will write to denote the activation of this hidden unit when the network is given a specific input...
"""Calculate algorithm 4, step 2, equation c) of Zhu et al [1]. 1 change: 1 addition & 0 deletions 1 sklearn/feature_selection/_from_model.py Original file line numberDiff line numberDiff line change @@ -501,5 +501,6 @@ def get_metadata_routing(self): def __sklearn_tags__...
Using CS (our NLHT algorithm), we are able to achieve perfect recovery of both the image [Fig. 1(e)] and its spatial spectrum [Fig. 1(f)]. To demonstrate the robustness of NLHT, we add noise to the system; evidently, the recon- struction is robust and the noise has a very ...
In the input graph, the global state is used to provide the algorithm with information about the system as a whole, in the case of our sparse representation, the composition of the base material. In the output graph, the global state contains the model predictions of the target variables. ...
For inverse synthetic aperture radar (ISAR) imagery, the scattering field of the target is usually composed of only a limited number of strong scattering centers, representing strong spatial sparsity. This paper derives a new autofocus algorithm to exploit the sparse apertures (SAs) data for ISAR ...
We have completed considering the public methods for the operation of the new class. But the main functionality of the class is to create the neural layer algorithm. So, let's get back to the feed forward and backpropagation passes. We have modernized the OpenCL program kernels to enable th...
A machine learning algorithm is an algorithm that can learn based on a set of data. Embodiments of machine learning algorithms can be designed to model high-level abstractions within a data set. For example, image recognition algorithms can be used to determine which of several categories to whi...